ArticleDiagnostics (Basel, Switzerland)2023
Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Ultrasound-MRI Fusion Deep Learning Radiomics Nomogram: Refining Prognostic Stratification for Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Hybrid fractal ConvXGBNet for brain tumor classification with log-cosh dice loss - cascaded V-Net based segmentation based on MRI images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Capuchin Red Kite–optimized Swin Transformer-based Convolutional Block Attention Module for Early Diagnosis and Classification of PneumoniaCurrent medical imaging · 2026Article
- Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals.Scientific reports · 2025Article
- Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images.Cancers · 2025Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- XAI-BT-EdgeNet: explainable edge-aware deep learning with squeeze-and-excitation for brain tumor detection and prediction.Frontiers in artificial intelligence · 2025Article
- LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.Journal of translational medicine · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm.Frontiers in oncology · 2024Article
- DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy.Biomedicines · 2023Article
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6 authors.
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Abstract
One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%.
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