X‐SCSANet: Explainable Stack Convolutional Self‐Attention Network for Brain Tumor Classification

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Brain tumors are devastating and shorten the patient’s life. It has an impact on physical, psychological, financial well‐being of both patients family members. Early diagnosis treatment can reduce patients’ chances survival. Detecting diagnosing brain cancers using MRI scans is time‐consuming requires expertise in that domain. Nowadays, instead traditional approaches to tumor analysis, several deep learning models used assist professionals mitigate time. This paper introduces a stack convolutional self‐attention network extracts important local global features from freely available scan dataset. Since medical domain one most sensitive fields, end‐users should put their trust model before automating classification. Therefore, Grad‐CAM method been updated better explain model’s output. Combining improves classification performance, with suggested reaching accuracy 96.44% relevant The proposed precision, specificity, sensitivity, F1‐score reported as 96.5%, 98.83%, 96.44%, 96.4%, respectively. Furthermore, layers’ insights examined acquire deeper knowledge decision‐making process.

Language: Английский

Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation

Methods, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский