A decision support system for multimodal brain tumor classification using deep learning

Sharif, Muhammad; Khan, Muhammad Attique; Alhussein, Musaed; Aurangzeb, Khursheed; Raza, Mudassar

doi:10.1007/s40747-021-00321-0

Cited by 128 publications

(65 citation statements)

References 45 publications

(44 reference statements)

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“…In this survey, recent literature regarding the detection of brain tumors is reviewed, and it is indicated that there is still room for improvement. During image acquisition, noise is included in MRI, and noise removal is an intricate task [2, [262][263][264]. Accurate segmentation is a difficult task [265], as brain tumors have tentacles and diffused structures [43,193,220,266].…”

Section: Limitations Of Existing's Machine/deep Learning Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

173

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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Section: Limitations Of Existing's Machine/deep Learning Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

173

View full text Add to dashboard Cite

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“…More recently, deep learning models have been shown to significantly contribute to medical image analysis for both segmentation and classification [15,16]. In deep learning, CNNs are used for classification as they are composed of several hidden layers such as convolutional, pooling, batch normalization, ReLU, and fully connected layers [17,18].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Bibi¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy

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“…With this substantial number of patients in the world, different medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and X-ray are used for the diagnosis [6][7][8]. Among these MRIs is the ordinary non-intrusive imaging procedure widely adopted in the clinical routine because it does not use any damaging ionizing radiations [9,10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Brain MRI Images Using Improved CornerNet Approach

et al. 2021

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The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

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A decision support system for multimodal brain tumor classification using deep learning

Cited by 128 publications

References 45 publications

Brain tumor detection and classification using machine learning: a comprehensive survey

Brain tumor detection and classification using machine learning: a comprehensive survey

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Analysis of Brain MRI Images Using Improved CornerNet Approach

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