A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

Hafeez, Hafiz Aamir; Elmagzoub, M. A.; Abdullah, Nurul Azma; Reshan, Mana Saleh Al; Gilanie, Ghulam; Alyami, Sultan; Hassan, Mahmood ul; Shaikh, Asadullah

doi:10.1109/access.2023.3273487

Cited by 16 publications

References 25 publications

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Brain tumor grade classification using multi‐step pre‐training

Mehmood,

Bajwa,

Anwar

2023

Int J Imaging Syst Tech

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Medical images offer a non‐invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data‐intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between the datasets used for pre‐training (e.g., ImageNet) and the target datasets, like medical images, negatively impacts the transfer learning results. Recently, many researchers have used additional pre‐training called domain‐adaptive pre‐training (DAPT) using the data from the target domain (e.g., medical images) before using the model on the target tasks to achieve superior performance. This study proposes a variant of DAPT by performing it on a subset of the architecture. It has achieved state‐of‐the‐art performance for brain tumor grading on the BraTS 2019 while being computationally efficient.

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Brain tumor grade classification using multi‐step pre‐training

Mehmood,

Bajwa,

Anwar

2023

Int J Imaging Syst Tech

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A Lightweight Multimodal Xception Network for Glioma Grading Using MRI Images

Liang,

Li,

Ren

et al. 2024

Int J Imaging Syst Tech

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Gliomas are the most common type of primary brain tumors, classified into low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs). There is a significant difference in survival rates between patients with different grades of gliomas, making imaging‐based grading a research hotspot. Current deep learning–based glioma grading algorithms face challenges, such as network complexity, low accuracy, and difficulty in large‐scale application. This paper proposes a multimodal, lightweight Xception grading network to address these issues. The network introduces convolutional block attention modules and employs dilated convolutions for spatial feature aggregation, reducing parameter count while maintaining the same receptive field. By integrating spatial and channel squeeze‐and‐excitation modules, it achieves more accurate feature learning, alongside improvements to the residual connection modules for critical information retention. Compared to existing methods, the proposed approach improves classification accuracy while maintaining a reduced parameter count. The network was trained and validated on 344 glioma cases (261 HGGs and 83 LGGs) and tested on 38 glioma cases (29 HGGs and 9 LGGs). Experimental results demonstrate that the network achieves an accuracy of 92.67% and an AUC of 0.9413 using a fully connected layer as the classifier. The features extracted using the improved Xception grading network achieved an accuracy of 93.42% when classified with KNN and RF classifiers. This study aims to provide diagnostic suggestions for clinical use through a simple, effective, and noninvasive multimodal medical imaging diagnostic method for LGG/HGG grading, thereby accelerating treatment decision‐making.

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Brain tumor classification for combining the advantages of multilayer dense net‐based feature extraction and hyper‐parameters tuned attentive dual residual generative adversarial network classifier using wild horse optimization

Anantharajan,

Gunasekaran,

Sujana

2024

NMR in Biomedicine

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In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN‐WHOA‐BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT, and Figshare datasets. Initially, the images are preprocessed to increase the quality of images and eliminate the unwanted noises. The preprocessing is performed with dual‐tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. Then, the extracted images are given to attentive dual residual generative adversarial network (ADRGAN) classifier for classifying the brain imageries. The ADRGAN weight parameters are tuned based on wild horse optimization algorithm (WHOA). The proposed method is executed in MATLAB. For the BraTS dataset, the ADRGAN‐WHOA‐BTD method achieved accuracy, sensitivity, specificity, F‐measure, precision, and error rates of 99.85%, 99.82%, 98.92%, 99.76%, 99.45%, and 0.15%, respectively. Then, the proposed technique demonstrated a runtime of 13 s, significantly outperforming existing methods.

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A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

Cited by 16 publications

References 25 publications

Brain tumor grade classification using multi‐step pre‐training

Brain tumor grade classification using multi‐step pre‐training

A Lightweight Multimodal Xception Network for Glioma Grading Using MRI Images

Brain tumor classification for combining the advantages of multilayer dense net‐based feature extraction and hyper‐parameters tuned attentive dual residual generative adversarial network classifier using wild horse optimization

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