Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Muhammad Nazir
-- 1325 2024-01-25 14:16:52 |
2 update references and layout
Rita Xu
 Meta information modification 1325 2024-01-26 02:21:02 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Nouman Noor, M.; Nazir, M.; Khan, S.A.; Song, O.; Ashraf, I. Gastrointestinal Disease Classification. Encyclopedia. Available online: https://encyclopedia.pub/entry/54350 (accessed on 04 May 2024).
Nouman Noor M, Nazir M, Khan SA, Song O, Ashraf I. Gastrointestinal Disease Classification. Encyclopedia. Available at: https://encyclopedia.pub/entry/54350. Accessed May 04, 2024.
Nouman Noor, Muhammad, Muhammad Nazir, Sajid Ali Khan, Oh-Young Song, Imran Ashraf. "Gastrointestinal Disease Classification" Encyclopedia, https://encyclopedia.pub/entry/54350 (accessed May 04, 2024).
Nouman Noor, M., Nazir, M., Khan, S.A., Song, O., & Ashraf, I. (2024, January 25). Gastrointestinal Disease Classification. In Encyclopedia. https://encyclopedia.pub/entry/54350
Nouman Noor, Muhammad, et al. "Gastrointestinal Disease Classification." Encyclopedia. Web. 25 January, 2024.
Gastrointestinal Disease Classification
Edit
This entry is adapted from the peer-reviewed paper 10.3390/electronics12071557

Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed.

gastrointestinal disease deep learning WCE images

1. Introduction

The digestive system is affected by gastrointestinal (GI) tract diseases. Medical imaging is a key component of these diseases’ diagnoses. Huge image data is difficult for radiologists and medical professionals to process, which makes it susceptible to inaccurate medical assessment [1]. The most prevalent digestive system disorders include ulcerative colitis, esophagitis, ulcers, and polyps that can develop into colorectal cancer. One of the leading causes of death worldwide is colorectal cancer [2].
According to a survey of the disease, 11% of women and 26% of men worldwide have been diagnosed with colorectal cancer [3]. In the US, 338,090 new cases of colorectal cancer were detected in 2021, with a 44% increase in fatalities [4]. Each year, 0.7 million new cases of illnesses are recorded globally [5]. Due to the high death rate, early diagnosis is exceedingly challenging. The development of ulcers in the GI tract is a serious sickness that goes hand in hand with GI malignant growth. As [6] reported, the most notable yearly predominance of ulcers retained in Spain was 141.9 per 100,000 individuals and the least was 57.75 in Sweden.
Many lesions are overlooked during a typical endoscopic examination because of the presence of feces and the organs’ complex architecture. The rate of missing polyps is very high, ranging from 21.4% to 26.8% [7], even when the bowel is cleansed to facilitate the diagnosis of cancer or its precursor lesions. Furthermore, it can be difficult to identify lesions because of their similarities between classes. A new treatment called wireless capsule endoscopy (WCE) [8] allows medical professionals to view the stomach, which was previously exceedingly difficult to access via standard endoscopy. In WCE, patients ingest a camera-encased capsule that records several images as it travels through the GI system. The experts (experienced gastroenterologists) stitch these images together to create a film, which is then analyzed to look for deformations. However, this approach has some drawbacks, including time requirements and a dearth of expertise. The primary issue with this procedure is the time commitment required for a manual diagnostic. Additionally, the poor contrast in the WCE images makes it difficult to see an ulcer properly [9]. As a result, there is a possibility that a doctor will overlook the ulcer throughout the detection phase. Another issue that arises during a diagnosis with the naked eye has to do with the similarity of color, texture, and shape variations [10][11].
Different techniques for the diagnosis of colorectal cancer and its precursor lesions utilizing the WCE images have been developed by numerous researchers [4][11]. These techniques have some basic steps such as contrast enhancement and noise removal from the image, followed by segmentation of diseased area within an image, important features extraction, and finally, classification of a specified class. An integral part of a computerized process is contrast enhancement. The primary goal of this phase is to increase an infected region’s intensity range to improve accuracy and retrieve pertinent features [12]. The resultant images are then passed into the segmentation phase for the detection of disease and subsequently for features extraction, but this step faces several difficulties (such as the alteration in the topology of the infected lesion and the similarity in color between the healthy and infected parts) that lower the segmentation accuracy. Later, a disease can be incorrectly classified into an inappropriate class as a result of the decrease in segmentation accuracy.
Recently, the performance of a deep convolutional neural network (CNN) for the identification and classification of medical infections has increased [13][14] as they outperformed traditional machine learning models. These techniques extracted features using pre-trained CNN models, which were then optimized for features. Due to memory and time constraints, the pre-trained CNN models are trained via transfer learning (TL). Most of the researchers are focusing more on using the complex CNN models as well as fusing multiple models and optimizing the features for achieving better accuracy. As WCE images are subject to several challenges and limitations, work on image improvement is an area upon which focus is needed. The quality of WCE images is not good because of volume and power limitations. As a result, WCE images exhibit weak contrast [15]. Furthermore, the great similarity between normal and abnormal frames further complicates the process of disease classification [16].

2. Gastrointestinal Disease Classification

The use of medical imaging to identify diseases has gained popularity in recent years, particularly in the field of the gastrointestinal system. Another active field of research is the classification of digestive illnesses. Although machine learning algorithms have demonstrated amazing performance in the literature [15][16], CNN algorithms outperform ML techniques and produce superior results [17].
Several research [18][19][20][21][22][23][24] works have examined the detection of abnormal frames in capsule endoscopy images, including the detection of tumors, Crohn’s disease, polyp, hemorrhages, ulcers, lymphangiectasia, and other intestinal lesions. Existing techniques often start with feature extraction and then apply a detection technique. The detection techniques either use multi-label approaches to find and classify various types of abnormality [19] or distinguish between frames with a lesion and normal ones [18]. These methods frequently extract features from the images’ texture and morphological analysis, statistical feature analysis, and color descriptors. These techniques either use region-based [20][21] or pixel-based [18][19] methodologies. Artificial neural networks (ANNs) and support vector machines (SVMs) are the two commonly utilized classification techniques in the literature [22][23][24]. Many other studies related to gastrointestinal diseases such as polyp, colon, and capsule endoscopy have been conducted in the literature [25][26][27][28][29][30].
DL models such as ResNet-50, VGG16, and Inception-V3 were used by Lee et al. [31] to categorize normal and ulcer GI pictures. Resnet-50 outperformed the other deep networks using this technique. A saliency-based strategy was presented by Khan et al. [32] to segment GI diseases, whereas DL architecture is employed for classification. They combined a YIQ color space with an HSI color space, which was then fed into a segmentation method based on contours. An automated technique for identifying an ulcer from the WCE frames was introduced by Yuan et al. [33]. To begin with, a saliency technique based on superpixels is used to define the boundaries of the ulcer zone. The level-by-level texture and color properties are then computed and combined to produce the final saliency map. Then, to achieve a recognition rate of 92.65%, the saliency max-pooling (SMP) and locality-constrained linear coding (LLC) techniques are combined. With a dataset of 854 photos, the authors of [34] proposed the VGGNet model, which was built on a CNN (convolutional neural network), to accurately identify gastrointestinal ulcers, even though these tests used images from a standard endoscopy. The dataset used in [35] comprised 5360 WCE images with ulcers and erosions and only 440 normal images; the authors created a model based on a CNN. This method’s detection accuracy was 90.8%. An attention-based DL architecture for classifying and localizing stomach diseases from WCE images was presented by Jain et al. [36]. They started by effectively classifying stomach illnesses using CNN. Later, for the localization of contaminated areas, they combined Grad-CAM++ and a unique SegNet. On a KID dataset, the proposed technique was tested, and it showed enhanced accuracy. For WCE video summarization, Lan et al. [37] developed a combination of unsupervised DL techniques. They employed several networks, including LSTM and autoencoder, among others. This research’s major goal was to assist medical professionals in their examination of the complete WCE videos. In [38], a gastrointestinal disease classification framework is proposed in which deep features are selected and fused from two deep models, i.e., ResNet-50 and ResNet-152. Subsequently, the features were optimized and it achieved 96.43% classification performance. In another work, [25], alimentary diseases such as Barret, Polyp, and Esophagitis were classified by applying discrete wavelet transform and CNN. This framework achieved a 96.65% accuracy on the Hyper Kvasir dataset.

References

  1. Ling, T.; Wu, L.; Fu, Y.; Xu, Q.; An, P.; Zhang, J.; Hu, S.; Chen, Y.; He, X.; Wang, J.; et al. A deep learn-ing-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying nar-row-band imaging endoscopy. Endoscopy 2020, 53, 469–477.
  2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
  3. Korkmaz, M.F. Artificial Neural Network by using HOG Features HOG_LDA_ANN. In Proceedings of the 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia, 14–16 September 2017; pp. 327–332.
  4. Li, S.; Cao, J.; Yao, J.; Zhu, J.; He, X.; Jiang, Q. Adaptive aggregation with self-attention network for gastrointestinal image classification. IET Image Process. 2022, 16, 2384–2397.
  5. Siegel, R.L.; Miller, K.; Jemal, A. Cancer statistics, 2015. CA Cancer J. Clin. 2015, 65, 5–29.
  6. Azhari, H.; King, J.; Underwood, F.; Coward, S.; Shah, S.; Ho, G.; Chan, C.; Ng, S.; Kaplan, G. The Global Incidence of Peptic Ulcer Disease at the Turn of the 21st Century: A Study of the Organization for Economic Co—Operation and Development (OECD). Am. J. Gastroenterol. 2018, 113, S682–S684.
  7. Kim, N.H.; Jung, Y.S.; Jeong, W.S.; Yang, H.-J.; Park, S.-K.; Choi, K.; Park, D.I. Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies. Intest. Res. 2017, 15, 411–418.
  8. Iddan, G.; Meron, G.; Glukhovsky, A.; Swain, P. Wireless capsule endoscopy. Nature 2000, 405, 417.
  9. Muruganantham, P.; Balakrishnan, S.M. Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization. J. Med Biol. Eng. 2022, 42, 157–168.
  10. Khan, M.A.; Khan, M.A.; Ahmed, F.; Mittal, M.; Goyal, L.M.; Hemanth, D.J.; Satapathy, S.C. Gastrointestinal diseases segmentation and classification based on duo-deep architectures. Pattern Recognit. Lett. 2019, 131, 193–204.
  11. Khan, M.A.; Sarfraz, M.S.; Alhaisoni, M.; Albesher, A.A.; Wang, S.; Ashraf, I. StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification. IEEE Access 2020, 8, 197969–197981.
  12. Amiri, Z.; Hassanpour, H.; Beghdadi, A. Feature extraction for abnormality detection in capsule endoscopy images. Biomed. Signal Process. Control. 2021, 71, 103219.
  13. Khan, M.; Ashraf, I.; Alhaisoni, M.; Damaševičius, R.; Scherer, R.; Rehman, A.; Bukhari, S. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics 2020, 10, 565.
  14. Cicceri, G.; De Vita, F.; Bruneo, D.; Merlino, G.; Puliafito, A. A deep learning approach for pressure ulcer prevention using wearable computing. Human-Centric Comput. Inf. Sci. 2020, 10, 5.
  15. Wong, G.L.H.; Ma, A.J.; Deng, H.; Ching, J.Y.L.; Wong, V.W.S.; Tse, Y.K.; Yip, T.C.-F.; Lau, L.H.-S.; Liu, H.H.-W.; Leung, C.M.; et al. Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding. APT—Aliment. Pharmacol. Therapeutics 2019, 49, 912–918.
  16. Wang, S.; Xing, Y.; Zhang, L.; Gao, H.; Zhang, H. Second glance framework (secG): Enhanced ulcer detection with deep learning on a large wireless capsule endoscopy dataset. In Proceedings of the Fourth International Workshop on Pattern Recognition, Nanjing, China, 28–30 June 2019; Volume 11198.
  17. Majid, A.; Khan, M.A.; Yasmin, M.; Rehman, A.; Yousafzai, A.; Tariq, U. Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection. Microsc. Res. Tech. 2020, 83, 562–576.
  18. Usman, M.A.; Satrya, G.; Shin, S.Y. Detection of small colon bleeding in wireless capsule endoscopy videos. Comput. Med Imaging Graph. 2016, 54, 16–26.
  19. Iakovidis, D.; Koulaouzidis, A. Automatic lesion detection in capsule endoscopy based on color saliency: Closer to an essential adjunct for reviewing software. Gastrointest Endosc. 2014, 80, 877–883.
  20. Noya, F.; Alvarez-Gonzalez, M.A.; Benitez, R. Automated angiodysplasia detection from wireless capsule endoscopy. In Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, South Korea, 11–15 July 2017; pp. 3158–3161.
  21. Li, B.; Meng, M.Q.-H. Texture analysis for ulcer detection in capsule endoscopy images. Image Vis. Comput. 2009, 27, 1336–1342.
  22. Fu, Y.; Zhang, W.; Mandal, M.; Meng, M.Q.-H. Computer-Aided Bleeding Detection in WCE Video. IEEE J. Biomed. Heal. Inform. 2013, 18, 636–642.
  23. Pan, G.; Yan, G.; Qiu, X.; Cui, J. Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network. J. Med Syst. 2010, 35, 1477–1484.
  24. Li, B.; Meng, M.Q.-H. Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images. IEEE Trans. Biomed. Eng. 2009, 56, 1032–1039.
  25. Mohapatra, S.; Pati, G.K.; Mishra, M.; Swarnkar, T. Gastrointestinal abnormality detection and classification using empirical wavelet transform and deep convolutional neural network from endoscopic images. Ain Shams Eng. J. 2023, 14, 101942.
  26. Koyama, S.; Okabe, Y.; Suzuki, Y.; Igari, R.; Sato, H.; Iseki, C.; Tanji, K.; Suzuki, K.; Ohta, Y. Differing clinical features between Japanese siblings with cerebrotendinous xanthomatosis with a novel compound heterozygous CYP27A1 mutation: A case report. BMC Neurol. 2022, 22, 193.
  27. Higuchi, N.; Hiraga, H.; Sasaki, Y.; Hiraga, N.; Igarashi, S.; Hasui, K.; Ogasawara, K.; Maeda, T.; Murai, Y.; Tatsuta, T.; et al. Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50. PLoS ONE 2022, 17, e0269728.
  28. Ji, X.; Xu, T.; Li, W.; Liang, L. Study on the classification of capsule endoscopy images. EURASIP J. Image Video Process. 2019, 2019, 1–7.
  29. Szczypiński, P.; Klepaczko, A.; Strzelecki, M. An Intelligent Automated Recognition System of Abnormal Structures in WCE Images. In Proceedings, Part I 6, Proceedings of the Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS 2011; Wroclaw, Poland, 23–25 May 2011, Lecture Notes in Computer Science 6678; Springer: Berlin, Heidelberg, 2011; pp. 140–147.
  30. Patel, V.; Armstrong, D.; Ganguli, M.P.; Roopra, S.; Kantipudi, N.; Albashir, S.; Kamath, M.V. Deep Learning in Gastrointestinal Endoscopy. Crit. Rev. Biomed. Eng. 2016, 44, 493–504.
  31. Lee, J.H.; Kim, Y.J.; Kim, Y.W.; Park, S.; Choi, Y.-I.; Park, D.K.; Kim, K.G.; Chung, J.-W. Spotting malignancies from gastric endoscopic images using deep learning. Surg. Endosc. 2019, 33, 3790–3797.
  32. Khan, M.A.; Lali, M.I.U.; Sharif, M.; Javed, K.; Aurangzeb, K.; Haider, S.I.; Altamrah, A.S.; Akram, T. An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection. IEEE Access 2019, 7, 46261–46277.
  33. Yuan, Y.; Wang, J.; Li, B.; Meng, M.Q.-H. Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis. IEEE Trans. Med Imaging 2015, 34, 2046–2057.
  34. Pogorelov, K.; Randel, K.R.; Griwodz, C.; Eskeland, S.L.; de Lange, T.; Johansen, D.; Spampinato, C.; Dang-Nguyen, D.-T.; Lux, M.; Schmidt, P.T.; et al. KVASIR: A multi-class image dataset for computer aided gastrointestinal disease detection. In Proceedings of the 8th ACM on Multimedia Systems Conference, Taipei, Taiwan, 20–23 June 2017; pp. 164–169.
  35. Borgli, H.; Thambawita, V.; Smedsrud, P.H.; Hicks, S.; Jha, D.; Eskeland, S.L.; Randel, K.R.; Pogorelov, K.; Lux, M.; Nguyen, D.T.D.; et al. HyperKvasir: A comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci. Data 2020, 7, 283.
  36. Jain, S.; Seal, A.; Ojha, A.; Yazidi, A.; Bures, J.; Tacheci, I.; Krejcar, O. A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images. Comput. Biol. Med. 2021, 137, 104789.
  37. Lan, L.; Ye, C. Recurrent generative adversarial networks for unsupervised WCE video summarization. Knowledge-Based Syst. 2021, 222, 106971.
  38. Alhajlah, M.; Noor, M.N.; Nazir, M.; Mahmood, A.; Ashraf, I.; Karamat, T. Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization. Comput. Mater. Contin. 2023, 75, 2227–2245.
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.
Upload a video for this entry
Information
Subjects: Computer Science, Artificial Intelligence
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Muhammad Nouman Noor
,
Muhammad Nazir
,
Sajid Ali Khan
,
Oh-Young Song
,
Imran Ashraf
View Times: 88
Entry Collection: Gastrointestinal Disease
Revisions: 2 times (View History)
Update Date: 26 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback