Explainable Deep Learning in Brain Tumors: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Tahir Hussain
,
Hayaru Shouno

Brain tumors (BT) present a considerable global health concern because of their high mortality rates across diverse age groups. A delay in diagnosing BT can lead to death. Therefore, a timely and accurate diagnosis through magnetic resonance imaging (MRI) is crucial. A radiologist makes the final decision to identify the tumor through MRI. However, manual assessments are flawed, time-consuming, and rely on experienced radiologists or neurologists to identify and diagnose a BT. Computer-aided classification models often lack performance and explainability for clinical translation, particularly in neuroscience research, resulting in physicians perceiving the model results as inadequate due to the black box model. Explainable deep learning (XDL) can advance neuroscientific research and healthcare tasks.

  • brain tumor (BT)
  • explainable deep learning (XDL)
  • magnetic resonance imaging (MRI)
  • Grad-CAM
  • CNN
  • classification and localization

1. Introduction

A brain tumor (BT) develops due to the abnormal growth of brain tissue which can harm brain cells [1][2]. It is a severe neurological disorder affecting people of all ages and genders [3][4][5]. The brain controls the functionality of the entire body, and tumors can alter both the behavior and structure of the brain. Therefore, brain damage can be harmful to the body [6]. According to projections by the American Cancer Society, there will be 1,958,310 cancer cases and 609,820 cancer-related deaths in the United States by 2023 [7]. Thus, early and accurate diagnosis through magnetic resonance imaging (MRI) can enhance the evaluation and prognosis of BT. Brain cancer can be treated in various ways: mainly including surgery, radiotherapy, and chemotherapy [8]. However, visually differentiating a BT from the surrounding brain parenchyma is difficult, and physically locating and removing pathological targets is nearly impossible [9].
In practice, MRI is often used to detect BT because it provides soft tissue images that assist physicians in localizing and defining tumor boundaries [10]. BT-MRI images have varying shapes, locations, and image contrasts, making it challenging for radiologists and neurologists to interpret them in multi-class (glioma, meningioma, pituitary, and no tumor) and binary-class (tumor and no tumor) classifications. However, early diagnosis is crucial for patients, and failure to provide one within a short time period could cause physical and financial discomfort [8]. To minimize these inconveniences, computer-aided diagnoses (CADs) can be used to detect BTs using multi-class and binary-class BT-MRI images [11]. The CAD system assists radiologists and neurologists in comprehensively interpreting, analyzing, and evaluating BT-MRI data within a short time period [12][13].
With the tremendous advances in artificial intelligence (AI) and deep learning (DL) brain imaging, image processing algorithms are helping physicians detect disorders as early as possible compared with human-led examinations [14]. Technological advancements in the medical field require reliable and efficient solutions because they are intimately connected to human life and mistakes could endanger life. An automated method is required to support medical diagnosis. Despite their potential, DL techniques have limitations in clinical settings [15]. However, in the traditional method, features are hand-crafted and rely on human interaction. In contrast, DL automatically extracts salient features to improve performance despite trade-offs in computational resources and training time. However, DL has shown much better results than traditional computer vision techniques in addressing these challenges [16]. A major problem of DL is that it only accepts input images and outputs results without providing a clear understanding of how information flows within the internal layers of the network. In sensitive applications, such as brain imaging, understanding the reasons behind a DL network’s prediction to obtain an accurate correction estimate is crucial. In [17], Huang et al. proposed an end-to-end ViT-AMC network using adaptive model fusion and multi-objective optimization to combine ViT with attention mechanism-integrated convolution (AMC) blocks. In laryngeal cancer grading, the ViT-AMC performed well. This research [18] presented an additional approach to recognizing laryngeal cancer in the early stages. This research developed a model to analyze laryngeal cancer utilizing the CNN method. Furthermore, the researchers evaluated the performance parameters compared to the existing approach in a series of trials for testing and validating the proposed model. The accuracy of this method was increased by 25% over the previous method. However, this model is inefficient in the modern technological age, where many datasets are being generated daily for medical diagnosis. Recently, explainable deep learning (XDL) has gained significant interest for studying the “black box” nature of DL networks in healthcare [15][19][20]. Using XDL methods, researchers, developers, and end users can develop transparent DL models that explain their decisions clearly. Medical end users are increasingly demanding that they feel more confident about DL techniques and are encouraged to use these systems to support clinical procedures. There are several DL-based solutions for the binary classification of tumors. However, almost all of these are black boxes. Consequently, they are less intelligible to humans. Regardless of human explainability, most existing methods aim to increase accuracy [21][22][23][24][25][26][27]. In addition, the model should be understood by medical professionals.

2. The Method for Classification and Localization of BT

Several studies on the classification of BT-MRI images using CNN [28][29][30][31][32], pre-trained CNN models using TL [33][34][35], and tumor, polyp, and ulcer detection using a cascade approach [36] have been reported with remarkable results. However, these models lack explainability [21][22][37][38]. Although many XDL methods have been proposed for natural image problems [39][40][41], relatively less attention has been paid to model explainability in the context of brain imaging applications [19][42]. Consequently, the lack of interpretability in the models has been a concern for radiologists and healthcare professionals that find the black-box nature of the models inadequate for their needs. However, the development of XDL frameworks can advance neuroscientific research and healthcare by providing transparent and interpretable models. For this purpose, a fast and efficient multi-classification BT and localization framework using an XDL model has to be developed. An explainable framework is required to explain why particular predictions were made [43]. Many researchers have applied attribution-based explainability approaches to interpret DL [44]. In attribution-based techniques for medical images, multiple methods, such as saliency maps [45], activation maps [46], CAM [47], Grad-CAM [48], Gradient [49], and shapely additive explanations, are used [50]. The adoption of CAMs in diverse applications has recently seen the emergence of CNN-based algorithms [47][48][51][52][53][54][55][56].
The Grad-CAM technique [48] has recently been proposed to visualize essential features from the input image conserved by the CNN layers for classification. Grad-CAM has been used in various disciplines; however, it is preferred by the health sector. An extension of Grad-CAM, segmented-Grad-CAM, which enables the creation of heat maps that show the relevance of specific pixels or regions within the input images for semantic segmentation, has been proposed [55]. It generates heatmaps that indicate the relevance of certain pixels or regions within the input images for segmentation. In [56], class-selective relevance mapping (CRM), CAM, and Grad-CAM approaches were presented for the visual interpretation of different medical imaging modalities (i.e., abdomen CT, brain MRI, and chest X-ray) to clarify the prediction of the CNN-based DL model. Yang and Ranka enhanced the Grad-CAM approach to provide a 3D heatmap to visually explain and categorize cases of Alzheimer’s disease [57]. These techniques have seldom been employed for binary tumor localization [58] and are not used for multi-class BT MRI localization for model explainability. However, they are often used to interpret classification judgments [44]. In [59], a modified version of the ResNet-152 model was introduced to identify cutaneous tumors. The performance of the model was comparable with that of 16 dermatologists, and Grad-CAM was used to enhance the interpretability of the model. The success of an algorithm is significant. However, to improve the performance of explainable models, a method has to be developed for evaluating the effectiveness of an explanation [60]. In [61], deep neural networks (particularly InceptionV3 and DenseNet121) were used to generate saliency maps for chest X-ray images. They then evaluated the effectiveness of these maps by measuring the degree of overlap between the maps and human-annotated ground truths. The maps generated by these models were found to have a high degree of overlap with human annotations, indicating their potential usefulness in explainable AI in medical imaging. Interestingly, the study reported in [62] identified the improved indicators via regions (XRAI) as an effective method for generating explanations for DL models. The various DL and XDL methods proposed for the automatic classification and localization of tumors are summarized in Table 1.
Table 1. Summarized related works on the classification and localization of BT.
Refs. Method Classification Mode of Explanation
[63] Feedforward neural network and DWT Binary-class classification Not used
[64] CNN Three-class BT classification Not used
[65] Multiscale CNN (MSCNN) Four-class BT Classification Not used
[66] Multi-pathway CNN Three-class BT classification Not used
[67] CNN Multi-class brain tumor Classification Not used
[68] CNN with Grad-CAM X-ray breast cancer mammogram image Heatmap
[69] CNN Chest X-ray image Heatmap
[70] CNN Multiple sclerosis MRI image Heatmap

Table 1 shows the related approaches discussed in [68][69][70]. These studies evaluated the performances of various CNN-based classifiers on medical images and compared their characteristics by generating heatmaps. Based on these studies, Grad-CAM exhibits the most accurate localization, which is desirable for heatmaps. A localized heatmap makes it easier to identify the features that significantly contribute to the CNN classification results. Unlike the feature maps of convolutional layers, these heatmaps show the hierarchy of the importance of locations in the feature maps that contribute to classification.

This entry is adapted from the peer-reviewed paper 10.3390/info14120642

References

  1. Amin, J.; Sharif, M.; Yasmin, M.; Fernandes, S.L. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognit. Lett. 2020, 139, 118–127.
  2. Amin, J.; Sharif, M.; Yasmin, M.; Fernandes, S.L. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Gener. Comput. Syst. 2018, 87, 290–297.
  3. Nazir, M.; Shakil, S.; Khurshid, K. Role of deep learning in brain tumor detection and classification (2015 to 2020): A review. Comput. Med Imaging Graph. 2021, 91, 101940.
  4. Tiwari, A.; Srivastava, S.; Pant, M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognit. Lett. 2020, 131, 244–260.
  5. Mohan, G.; Subashini, M.M. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed. Signal Process. Control 2018, 39, 139–161.
  6. Ayadi, W.; Charfi, I.; Elhamzi, W.; Atri, M. Brain tumor classification based on hybrid approach. Vis. Comput. 2022, 38, 107–117.
  7. Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 2023, 73, 17–48.
  8. Dandıl, E.; Çakıroğlu, M.; Ekşi, Z. Computer-aided diagnosis of malign and benign brain tumors on MR images. In Proceedings of the ICT Innovations 2014: World of Data, Ohrid, Macedonia, 9–12 September 2014; Springer: Cham, Switzerland, 2015; pp. 157–166.
  9. Tu, L.; Luo, Z.; Wu, Y.L.; Huo, S.; Liang, X.J. Gold-based nanomaterials for the treatment of brain cancer. Cancer Biol. Med. 2021, 18, 372.
  10. Miner, R.C. Image-guided neurosurgery. J. Med. Imaging Radiat. Sci. 2017, 48, 328–335.
  11. Paul, J.; Sivarani, T. Computer aided diagnosis of brain tumor using novel classification techniques. J. Ambient. Intell. Humaniz. Comput. 2021, 12, 7499–7509.
  12. Abd El-Wahab, B.S.; Nasr, M.E.; Khamis, S.; Ashour, A.S. BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification. Health Inf. Sci. Syst. 2023, 11, 3.
  13. Khan, M.S.I.; Rahman, A.; Debnath, T.; Karim, M.R.; Nasir, M.K.; Band, S.S.; Mosavi, A.; Dehzangi, I. Accurate brain tumor detection using deep convolutional neural network. Comput. Struct. Biotechnol. J. 2022, 20, 4733–4745.
  14. Wijethilake, N.; Meedeniya, D.; Chitraranjan, C.; Perera, I.; Islam, M.; Ren, H. Glioma survival analysis empowered with data engineering—A survey. IEEE Access 2021, 9, 43168–43191.
  15. Yang, G.; Ye, Q.; Xia, J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inf. Fusion 2022, 77, 29–52.
  16. O’Mahony, N.; Campbell, S.; Carvalho, A.; Harapanahalli, S.; Hernandez, G.V.; Krpalkova, L.; Riordan, D.; Walsh, J. Deep learning vs. traditional computer vision. In Advances in Computer Vision, Proceedings of the 2019 Computer Vision Conference (CVC), Las Vegas, NV, USA, 2–3 May 2019; Springer: Cham, Switzerland, 2020; Volume 1, pp. 128–144.
  17. Huang, P.; He, P.; Tian, S.; Ma, M.; Feng, P.; Xiao, H.; Mercaldo, F.; Santone, A.; Qin, J. A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images. IEEE Trans. Med. Imaging 2022, 42, 15–28.
  18. Huang, P.; Tan, X.; Zhou, X.; Liu, S.; Mercaldo, F.; Santone, A. FABNet: Fusion attention block and transfer learning for laryngeal cancer tumor grading in P63 IHC histopathology images. IEEE J. Biomed. Health Inform. 2021, 26, 1696–1707.
  19. Gulum, M.A.; Trombley, C.M.; Kantardzic, M. A review of explainable deep learning cancer detection models in medical imaging. Appl. Sci. 2021, 11, 4573.
  20. Ahmed Salman, S.; Lian, Z.; Saleem, M.; Zhang, Y. Functional Connectivity Based Classification of ADHD Using Different Atlases. In Proceedings of the 2020 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China, 18–20 December 2020; pp. 62–66.
  21. Shah, H.A.; Saeed, F.; Yun, S.; Park, J.H.; Paul, A.; Kang, J.M. A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet. IEEE Access 2022, 10, 65426–65438.
  22. Asif, S.; Yi, W.; Ain, Q.U.; Hou, J.; Yi, T.; Si, J. Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access 2022, 10, 34716–34730.
  23. Amin, J.; Sharif, M.; Gul, N.; Yasmin, M.; Shad, S.A. Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognit. Lett. 2020, 129, 115–122.
  24. Rehman, A.; Khan, M.A.; Saba, T.; Mehmood, Z.; Tariq, U.; Ayesha, N. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc. Res. Tech. 2021, 84, 133–149.
  25. Wijethilake, N.; Islam, M.; Meedeniya, D.; Chitraranjan, C.; Perera, I.; Ren, H. Radiogenomics of glioblastoma: Identification of radiomics associated with molecular subtypes. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-Oncology, Proceedings of the Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, 4–8 October 2020; Proceedings 3; Springer: Cham, Switzerland, 2020; pp. 229–239.
  26. Wijethilake, N.; Meedeniya, D.; Chitraranjan, C.; Perera, I. Survival prediction and risk estimation of Glioma patients using mRNA expressions. In Proceedings of the 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA, 26–28 October 2020; pp. 35–42.
  27. Salman, S.A.; Zakir, A.; Takahashi, H. Cascaded deep graphical convolutional neural network for 2D hand pose estimation. In Proceedings of the International Workshop on Advanced Imaging Technology (IWAIT) 2023; Nakajima, M., Kim, J.G., deok Seo, K., Yamasaki, T., Guo, J.M., Lau, P.Y., Kemao, Q., Eds.; International Society for Optics and Photonics, SPIE: San Diego, CA, USA, 2023; Volume 12592, p. 1259215.
  28. Badža, M.M.; Barjaktarović, M.Č. Classification of brain tumors from MRI images using a convolutional neural network. Appl. Sci. 2020, 10, 1999.
  29. Mzoughi, H.; Njeh, I.; Wali, A.; Slima, M.B.; BenHamida, A.; Mhiri, C.; Mahfoudhe, K.B. Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J. Digit. Imaging 2020, 33, 903–915.
  30. Ayadi, W.; Elhamzi, W.; Charfi, I.; Atri, M. Deep CNN for brain tumor classification. Neural Process. Lett. 2021, 53, 671–700.
  31. Abiwinanda, N.; Hanif, M.; Hesaputra, S.T.; Handayani, A.; Mengko, T.R. Brain tumor classification using convolutional neural network. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018, Prague, Czech Republic, 3–8 June 2018; Springer: Cham, Switzerland, 2019; Volume 1, pp. 183–189.
  32. Sultan, H.H.; Salem, N.M.; Al-Atabany, W. Multi-classification of brain tumor images using deep neural network. IEEE Access 2019, 7, 69215–69225.
  33. Çinar, A.; Yildirim, M. Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med. Hypotheses 2020, 139, 109684.
  34. Rehman, A.; Naz, S.; Razzak, M.I.; Akram, F.; Imran, M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst. Signal Process. 2020, 39, 757–775.
  35. Mehrotra, R.; Ansari, M.; Agrawal, R.; Anand, R. A transfer learning approach for AI-based classification of brain tumors. Mach. Learn. Appl. 2020, 2, 100003.
  36. Rahim, T.; Usman, M.A.; Shin, S.Y. A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging. Comput. Med. Imaging Graph. 2020, 85, 101767.
  37. Kang, J.; Ullah, Z.; Gwak, J. Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 2021, 21, 2222.
  38. Rai, H.M.; Chatterjee, K. 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimed. Tools Appl. 2021, 80, 36111–36141.
  39. Intagorn, S.; Pinitkan, S.; Panmuang, M.; Rodmorn, C. Helmet Detection System for Motorcycle Riders with Explainable Artificial Intelligence Using Convolutional Neural Network and Grad-CAM. In Proceedings of the International Conference on Multi-disciplinary Trends in Artificial Intelligence, Hyberabad, India, 17–19 November 2022; Springer: Cham, Switzerland, 2022; pp. 40–51.
  40. Dworak, D.; Baranowski, J. Adaptation of Grad-CAM method to neural network architecture for LiDAR pointcloud object detection. Energies 2022, 15, 4681.
  41. Lucas, M.; Lerma, M.; Furst, J.; Raicu, D. Visual explanations from deep networks via Riemann-Stieltjes integrated gradient-based localization. arXiv 2022, arXiv:2205.10900.
  42. Chen, H.; Gomez, C.; Huang, C.M.; Unberath, M. Explainable medical imaging AI needs human-centered design: Guidelines and evidence from a systematic review. NPJ Digit. Med. 2022, 5, 156.
  43. Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115.
  44. Singh, A.; Sengupta, S.; Lakshminarayanan, V. Explainable deep learning models in medical image analysis. J. Imaging 2020, 6, 52.
  45. Lévy, D.; Jain, A. Breast mass classification from mammograms using deep convolutional neural networks. arXiv 2016, arXiv:1612.00542.
  46. Van Molle, P.; De Strooper, M.; Verbelen, T.; Vankeirsbilck, B.; Simoens, P.; Dhoedt, B. Visualizing convolutional neural networks to improve decision support for skin lesion classification. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Proceedings of the First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 16–20 September 2018; Proceedings 1; Springer: Cham, Switzerland, 2018; pp. 115–123.
  47. Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2921–2929.
  48. Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 618–626.
  49. Eitel, F.; Ritter, K.; Alzheimer’s Disease Neuroimaging Initiative (ADNI). Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, Proceedings of the Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, 17 October 2019; Proceedings 9; Springer: Cham, Switzerland, 2019; pp. 3–11.
  50. Young, K.; Booth, G.; Simpson, B.; Dutton, R.; Shrapnel, S. Deep neural network or dermatologist? In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, Proceedings of the Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, 17 October 2019; Proceedings 9; Springer: Cham, Switzerland, 2019; pp. 48–55.
  51. Aslam, F.; Farooq, F.; Amin, M.N.; Khan, K.; Waheed, A.; Akbar, A.; Javed, M.F.; Alyousef, R.; Alabdulijabbar, H. Applications of gene expression programming for estimating compressive strength of high-strength concrete. Adv. Civ. Eng. 2020, 2020, 8850535.
  52. Hacıefendioğlu, K.; Demir, G.; Başağa, H.B. Landslide detection using visualization techniques for deep convolutional neural network models. Nat. Hazards 2021, 109, 329–350.
  53. Jiang, P.T.; Zhang, C.B.; Hou, Q.; Cheng, M.M.; Wei, Y. Layercam: Exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 2021, 30, 5875–5888.
  54. Meng, Q.; Wang, H.; He, M.; Gu, J.; Qi, J.; Yang, L. Displacement prediction of water-induced landslides using a recurrent deep learning model. Eur. J. Environ. Civ. Eng. 2023, 27, 2460–2474.
  55. Vinogradova, K.; Dibrov, A.; Myers, G. Towards interpretable semantic segmentation via gradient-weighted class activation mapping (student abstract). In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 13943–13944.
  56. Kim, I.; Rajaraman, S.; Antani, S. Visual interpretation of convolutional neural network predictions in classifying medical image modalities. Diagnostics 2019, 9, 38.
  57. Yang, C.; Rangarajan, A.; Ranka, S. Visual explanations from deep 3D convolutional neural networks for Alzheimer’s disease classification. In Proceedings of the AMIA Annual Symposium Proceedings, San Francisco, CA, USA, 3–7 November 2018; American Medical Informatics Association: Bethesda, MD, USA, 2018; Volume 2018, p. 1571.
  58. ÖZTÜRK, T.; KATAR, O. A Deep Learning Model Collaborates with an Expert Radiologist to Classify Brain Tumors from MR Images. Turk. J. Sci. Technol. 2022, 17, 203–210.
  59. Han, S.S.; Kim, M.S.; Lim, W.; Park, G.H.; Park, I.; Chang, S.E. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Investig. Dermatol. 2018, 138, 1529–1538.
  60. Holzinger, A.; Carrington, A.; Müller, H. Measuring the quality of explanations: The system causability scale (SCS) comparing human and machine explanations. KI-Künstl. Intell. 2020, 34, 193–198.
  61. Arun, N.; Gaw, N.; Singh, P.; Chang, K.; Aggarwal, M.; Chen, B.; Hoebel, K.; Gupta, S.; Patel, J.; Gidwani, M.; et al. Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiol. Artif. Intell. 2021, 3, e200267.
  62. Kapishnikov, A.; Bolukbasi, T.; Viégas, F.; Terry, M. Xrai: Better attributions through regions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 4948–4957.
  63. Ullah, Z.; Farooq, M.U.; Lee, S.H.; An, D. A hybrid image enhancement based brain MRI images classification technique. Med. Hypotheses 2020, 143, 109922.
  64. Bodapati, J.D.; Shaik, N.S.; Naralasetti, V.; Mundukur, N.B. Joint training of two-channel deep neural network for brain tumor classification. Signal Image Video Process. 2021, 15, 753–760.
  65. Yazdan, S.A.; Ahmad, R.; Iqbal, N.; Rizwan, A.; Khan, A.N.; Kim, D.H. An efficient multi-scale convolutional neural network based multi-class brain MRI classification for SaMD. Tomography 2022, 8, 1905–1927.
  66. Díaz-Pernas, F.J.; Martínez-Zarzuela, M.; Antón-Rodríguez, M.; González-Ortega, D. A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare 2021, 9, 153.
  67. Kibriya, H.; Masood, M.; Nawaz, M.; Nazir, T. Multiclass classification of brain tumors using a novel CNN architecture. Multimed. Tools Appl. 2022, 81, 29847–29863.
  68. Lizzi, F.; Scapicchio, C.; Laruina, F.; Retico, A.; Fantacci, M.E. Convolutional neural networks for breast density classification: Performance and explanation insights. Appl. Sci. 2021, 12, 148.
  69. Saporta, A.; Gui, X.; Agrawal, A.; Pareek, A.; Truong, S.Q.; Nguyen, C.D.; Ngo, V.D.; Seekins, J.; Blankenberg, F.G.; Ng, A.Y.; et al. Benchmarking saliency methods for chest X-ray interpretation. Nat. Mach. Intell. 2022, 4, 867–878.
  70. Zhang, Y.; Hong, D.; McClement, D.; Oladosu, O.; Pridham, G.; Slaney, G. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J. Neurosci. Methods 2021, 353, 109098.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback