PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images

Muezzinoglu, Taha; Baygin, Nursena; Tuncer, Ilknur; Barua, Prabal Datta; Bayğın, Mehmet; Doğan, Şengül; Tuncer, Türker; Palmer, Elizabeth E.; Cheong, Kang Hao; Acharya, U. Rajendra

doi:10.1007/s10278-023-00789-x

Cited by 26 publications

(9 citation statements)

References 51 publications

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“…Al Kadi et al [22] focused on extracting histopathological features, without applying any segmentation method, and achieved an accuracy of 92% accuracy using a fuzzy clustering machine for classification. In contrast, Muezzinoglu et al [23] proposed the ResNet50 transfer learning technique, classifying multiple types of brain tumors with a 98% accuracy. Georgiardis et al [24] attained an accuracy of 93%, though segmentation was not part of their study.…”

Section: Discussionmentioning

confidence: 99%

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Singh,

Saxena

2024

Int. j. electr. comput. eng. syst. (Online)

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Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.

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Section: Discussionmentioning

confidence: 99%

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Singh,

Saxena

2024

Int. j. electr. comput. eng. syst. (Online)

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“…CNN-based approaches, exemplified by Talukder et al, 2023 [13] and Tabatabaei et al, 2023 [48] on the Figshare dataset with accuracies of 99.68% and 99.30%, respectively, demonstrate noteworthy efficacy. On the Kaggle dataset, alongside Proposed-Swin (ViT), other CNN-based models such as Rahman and Islam [82], Muezzinoglu et al [83], and Ali et al [84] also exhibit high accuracy rates. However, it is essential to highlight that Proposed-Swin (ViT) not only surpasses these CNN-based models but excels as a benchmark for superior performance in brain tumor classification.…”

Section: Comparison With Cutting-edge Methodsmentioning

confidence: 99%

A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

Pacal

2024

Int. J. Mach. Learn. & Cyber.

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Serious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types and stages, and potential errors in interpretation hinder the achievement of precise and prompt diagnoses. The rapid identification of brain tumors plays a pivotal role in ensuring patient safety. Deep learning-based systems hold promise in aiding radiologists to make diagnoses swiftly and accurately. In this study, we present an advanced deep learning approach based on the Swin Transformer. The proposed method introduces a novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along with a rescaled model. This enhancement aims to improve classification accuracy, reduce memory usage, and simplify training complexity. The Residual-based MLP (ResMLP) replaces the traditional MLP in the Swin Transformer, thereby improving accuracy, training speed, and parameter efficiency. We evaluate the Proposed-Swin model on a publicly available brain MRI dataset with four classes, using only test data. Model performance is enhanced through the application of transfer learning and data augmentation techniques for efficient and robust training. The Proposed-Swin model achieves a remarkable accuracy of 99.92%, surpassing previous research and deep learning models. This underscores the effectiveness of the Swin Transformer with HSW-MSA and ResMLP improvements in brain tumor diagnosis. This method introduces an innovative diagnostic approach using HSW-MSA and ResMLP in the Swin Transformer, offering potential support to radiologists in timely and accurate brain tumor diagnosis, ultimately improving patient outcomes and reducing risks.

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“…For each layer, the feature maps of the previous layers were used as input and the feature maps of the current layers were produced as output. The CNN model has many advantages: it makes features more widespread and diversified through convolutional layers, reduces the number of parameters by pooling layers, and reduces overfitting problems through dropout layers [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

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PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images

Cited by 26 publications

References 51 publications

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

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