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Engineering, Biomedical
The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization.
- deep learning
- brain tumor
- magnetic resonance imaging
- classification
- neural network
1. Introduction
The development of a brain tumor can occur when there is an abnormal proliferation of cells within the brain tissues. Tumors have been identified by the World Health Organization (WHO) as the second most significant contributor to global mortality [1,2]. Brain tumors can be categorized into two main types: benign and malignant. In most instances, benign tumors are not considered a substantial risk to an individual’s health. It is primarily due to their comparatively slower growth rate than malignant tumors, lack of ability to infiltrate adjacent tissues or cells, and inability to metastasize. Their recurrence is generally uncommon after the surgical removal of benign tumors.
Compared to benign tumors, malignant tumors can infiltrate adjacent tissues and organs, and if not promptly and effectively managed, they can result in significant physiological dysfunction. Detecting brain tumors in their earliest stages is crucial for optimizing the survival rate of patients. Gliomas, meningioma, and pituitary tumors are the three most frequently diagnosed types of brain tumors. Glioma is a neoplasm originating from the glial cells that encompass and provide support to neurons. The cellular composition of these structures includes astrocytes, oligodendrocytes, and ependymal cells. A pituitary tumor is formed within the pituitary gland. A meningioma is a tumor originating within the meninges, the three layers of tissue between the skull and the brain. According to the cited source, it has been established that meningiomas are classified as benign tumors, while gliomas are categorized as malignant tumors. Additionally, pituitary tumors have been identified as benign. The dissimilarity above represents the most notable differentiation among these three cancer variants [3,4,5].
Various symptoms can be produced by benign and malignant brain tumors, depending on factors such as their size, location, and growth rate. The symptoms of primary brain tumors may exhibit variability among individual patients. Glioma has the potential to induce various symptoms, including aphasia, visual impairments or loss, cognitive impairments, difficulties with walking or balance, and other associated manifestations. A meningioma is often associated with mild symptoms, including visual disturbances and morning migraines. Pituitary tumors can exert pressure on the optic nerve, leading to symptoms such as migraines, vision disorders, and diplopia [6,7].
Hence, it is imperative to distinguish among these diverse tumor classifications to precisely diagnose a patient and determine the optimal course of treatment. The expertise of radiologists significantly influences the speed at which they can detect brain malignancies. Although magnetic resonance imaging (MRI) presents challenges due to its dependence on human interpretation and the complexity of processing large volumes of data, it is commonly employed to categorize different forms of cancer. Biopsies are commonly employed in identifying and managing brain lesions, although their utilization before definitive brain surgery is infrequent. Developing a comprehensive diagnostic instrument for detecting and classifying tumors based on MR images is imperative [8]. The implementation of this approach will effectively mitigate the occurrence of excessive operations and uphold the impartiality of the diagnostic procedure. The healthcare industry has been significantly influenced by recent technological advancements, particularly in the fields of artificial intelligence (AI) and machine learning (ML) [9,10,11,12]. Solutions to various healthcare challenges, such as imaging, have been successfully identified [13,14,15,16,17,18]. Various machine-learning techniques have been developed to provide radiologists with unusual insights into the recognition and classification of MR images. Medical imaging techniques are widely recognized as highly effective and widely utilized modalities for cancer detection. These methodologies facilitate the identification and detection of malignant neoplasms. The methodology holds significance due to its non-invasive nature, as it does not require invasive procedures [19,20].
MRI and other imaging modalities are commonly employed in medical interventions because they produce distinct visual representations of brain tissue, facilitating the identification and categorization of diverse brain malignancies. Brain tumors exhibit various sizes, dimensions, and densities [21]. Moreover, it is worth noting that tumors can exhibit similar appearances, even when they possess distinct pathogenic characteristics. A substantial quantity of images within the database posed a significant challenge in classifying MR images utilizing specialized neural networks. Due to the ability to generate MR images in multiple planes, there is a potential for increased database sizes. In order to obtain the desired classification outcome, it is necessary to preprocess MR images before integrating them into different networks. The Convolutional Neural Network (CNN) is employed to solve this problem, benefiting from several advantages, such as reduced preprocessing and feature engineering requirements. A network with lower complexity necessitates a reduced allocation of resources for implementation and training compared to one with higher complexity. Resource limitations hinder the utilization of the system for medical diagnostics or on mobile platforms. The method must be relevant to brain disorders for daily regular clinical diagnosis.
2. Deep Learning in Brain Tumor Classification from MRI
It is challenging to distinguish between various varieties of brain tumors. The authors [22] examined the clinical applications of DL in radiography and outlined the processes necessary for a DL project in this discipline. They also discussed the potential clinical applications of DL in various medical disciplines. In a few radiology applications, DL has demonstrated promising results, but the technology is not yet developed enough to replace the diagnostic occupation of a radiologist [23]. There is a possibility that DL algorithms and radiologists will collaborate to enhance diagnostic effectiveness and efficiency. Numerous studies have investigated the capability of MRI to identify and classify brain tumors utilizing a variety of research methodologies. Afshar et al. developed a modified version of the CapsNet architecture for categorizing the primary brain tumor consisting of 3064 images using tumor boundaries as supplementary inputs to increase effort, surpass previous techniques, and achieve a classification rate of 90.89% [24]. Gumaei et al. proposed a brain tumor classification method using hybrid feature extraction techniques and RELM. The authors preprocessed brain images using min–max normalization, extracted features using the hybrid method, classified them using RELM, and achieved a maximum accuracy of 94.23% [25].
Kaplan et al. proposed brain tumor classification models using nLBP and αLBP feature extraction methods. These models accurately classified the most common brain tumor types, including glioma, meningioma, and pituitary tumors, and achieved a high accuracy of 95.56% using the nLBPD = 1 feature extraction method and KNN model [19]. Rezaei et al. developed an integrated approach for segmenting and classifying brain tumors in MRI images. The methods included noise removal, SVM-based segmentation, feature extraction, and selection using DE. Tumor slices were classified using KNN, WSVM, and HIK-SVM classifiers. Combined with MODE-based ensemble techniques, these classifiers achieved a 92.46% accuracy rate [26]. Fouad et al. developed a brain tumor classification method using HDWT-HOG feature descriptors and the WOA for feature reduction. The approach utilized the Bagging ensemble techniques and achieved an average accuracy of 96.4% with Bagging, and, when used, Boosting attained 95.8% [27].
Ayadi et al. presented brain tumor classification techniques using normalization, dense speeded-up robust features, and the histogram of gradient approaches to enhance the image quality and generate a discriminative feature. In addition, they used SVM for classification and achieved a 90.27% accuracy on the benchmarked dataset [28]. Srujan et al. built a DL system with sixteen layers of CNN to classify the tumor types by leveraging activation functions like ReLU and Adam optimizer, and the system achieved a 95.36% accuracy [29]. Tejaswini et al. proposed a CNN model to detect meningioma, glioma, and pituitary brain tumors with an average training accuracy of 92.79% and validation accuracy of 87.16%; in addition, the tumor region segmentation was performed using Otsu thresholding, Fuzzy c-means, and watershed techniques [30]. Huang et al. developed a CNNBCN to classify brain tumors. The network structure was generated using a random graph algorithm, achieving an accuracy of 95.49% [31].
Ghassemi et al. suggested a DL framework for brain tumor classification. The authors used pre-trained networks as GAN discriminators to extract robust features and learn MR image structures. By replacing the fully connected layers and incorporating techniques like data augmentation and dropout, the method achieved a 95.6% accuracy using fivefold cross-validation [32]. Deepak et al. combined the CNN feature with SVM for the medical image classification of brain tumors. The automated system achieved an accuracy of 95.82% evaluated on the fivefold cross-validation procedure, outperforming the state-of-the-art method [33]. Noreen et al. adapted fine-tuned pre-trained networks, such as InceptionV3 and Xception, for identifying brain tumors. The models were integrated with various ML methods, namely Softmax, SVM, Random Forest, and KNN, and achieved a 94.34% accuracy with the InceptionV3 ensemble [34]. Shaik et al. addressed the challenging task of brain tumor classification in medical image analysis. The authors introduced a multi-level attention mechanism, MANet, which combined spatial and cross-channel attention to prioritize tumors and maintain cross-channel temporal dependencies. The method achieved a 96.51% accuracy for primary brain tumor classification [35].
Ahmad et al. proposed a deep generative neural network for brain tumor classification. The method combined variational auto encoders and generative adversarial networks to generate realistic brain tumor MRI images and achieved an accuracy of 96.25% [36]. Alanazi et al. proposed a deep transfer learning model for the early diagnosis of brain tumor subtypes. The method involved constructing isolated CNN models and adjusting the weights of a 22-layer CNN model using transfer learning. The developed model obtained 95.75- and 96.89-percent accuracies on MRI images [37]. Almalki et al. used an ML approach with MRI to promptly diagnose brain tumor severity (glioma, meningioma, pituitary, and no tumor). They extracted Gaussian and nonlinear scale features, capturing small details by breaking MRIs into 8 × 8-pixel images. The strongest features were selected and segmented into 400 Gaussian and 400 nonlinear scale features, and they were hybridized with each MRI. They obtained a 95.33% accuracy using the SVM classifier [38]. Kumar et al. compared three CNN models (AlexNet, ResNet50, and InceptionV3) to classify the primary tumor types and employed data augmentation techniques. The results showed that AlexNet achieved an accuracy of 96.2%, surpassing the other models [39].
Swati et al. employed a pre-trained deep CNN model and proposed a block-wise fine-tuning technique using transfer learning. This approach was evaluated using a standardized dataset consisting of T1-weighted images. Using minimal preprocessing techniques and excluding handcrafted features, the strategy demonstrated an accuracy of 94.82% with VGG19, VGG16 achieved 94.65%, and AlexNet achieved 89.95% when evaluated using a fivefold cross-validation methodology [40]. Ekong et al. integrated depth-wise separable convolutions with Bayesian techniques to precisely classify and predict brain cancers. The recommended technique demonstrated superior performance compared to existing methods in terms of an accuracy of 94.32% [41].
Asiri et al. enhanced computer-aided systems and facilitated physician learning using artificially generated medical imaging data. A deep learning technique, a Generative Adversarial Network (GAN), was employed, wherein a generator and a discriminator engage in a competitive process to generate precise MRI data. The proposed methodology demonstrated a notable level of precision, with an accuracy rate of 96%. The evaluation of this approach was conducted using a dataset comprising MRI scans collected from various Chinese hospitals throughout the period spanning from 2005 to 2020 [42]. Shilaskar et al. proposed a system comprising three main components: preprocessing, HOG for feature extraction, and classification. The results indicated varying levels of accuracy when employing multiple machine learning classifiers, including SVM, Gradient Boosting, KNN, XG Boost, and Logistic Regression, with the XG Boost classifier attaining the highest accuracy rate of 92.02% [43].
This entry is adapted from the peer-reviewed paper 10.3390/brainsci13091320
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