ArticleMedical & biological engineering & computing2024
Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics.
Article in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 27 citations in OpenAlex.
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- KNN algorithm for accurate identification of IFP lesions in the knee joint: a multimodal MRI study.Scientific reports · 2025Article
- Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.Scientific reports · 2025Article
- Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.BMC medical imaging · 2025Article
- Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy.Medical & biological engineering & computing · 2025Article
- Classification of the stages of Alzheimer's disease based on three-dimensional lightweight neural networks.PeerJ. Computer science · 2025Article
- CLGB-Net: fusion network for identifying local and global information of lesions in digital mammography images.Frontiers in oncology · 2025Article
- Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.Scientific reports · 2024Article
- A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images.Scientific reports · 2024Article
- A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection.Neuropsychiatric disease and treatment · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The early diagnosis of brain tumors is critical in the area of healthcare, owing to the potentially life-threatening repercussions unstable growths within the brain can pose to individuals. The accurate and early diagnosis of brain tumors enables prompt medical intervention. In this context, we have established a new model called MTAP to enable a highly accurate diagnosis of brain tumors. The MTAP model addresses dataset class imbalance by utilizing the ADASYN method, employs a network pruning technique to reduce unnecessary weights and nodes in the neural network, and incorporates Avg-TopK pooling method for enhanced feature extraction. The primary goal of our research is to enhance the accuracy of brain tumor type detection, a critical aspect of medical imaging and diagnostics. The MTAP model introduces a novel classification strategy for brain tumors, leveraging the strength of deep learning methods and novel model refinement techniques. Following comprehensive experimental studies and meticulous design, the MTAP model has achieved a state-of-the-art accuracy of 99.69%. Our findings indicate that the use of deep learning and innovative model refinement techniques shows promise in facilitating the early detection of brain tumors. Analysis of the model's heat map revealed a notable focus on regions encompassing the parietal and temporal lobes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.