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

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rahad Khan, Rafiqul Islam
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引用次数: 0

Abstract

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

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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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