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.

Abstract Image

X-SCSANet:用于脑肿瘤分类的可解释堆栈卷积自注意网络
脑瘤是毁灭性的,会缩短病人的生命。它对患者和家庭成员的身体、心理和经济健康都有影响。早期诊断和治疗可以降低患者的生存机会。使用核磁共振成像扫描检测和诊断脑癌非常耗时,而且需要该领域的专业知识。目前,一些深度学习模型取代了传统的脑肿瘤分析方法,以帮助专业人员并节省时间。本文介绍了一种堆栈卷积自关注网络,该网络从免费的MRI扫描数据集中提取重要的局部和全局特征。由于医疗领域是最敏感的领域之一,最终用户在自动化肿瘤分类之前应该信任深度学习模型。因此,已经更新了Grad-CAM方法,以更好地解释模型的输出。局部特征和全局特征的结合提高了脑肿瘤的分类性能,该模型在相关数据集上的准确率达到96.44%。该模型的精密度、特异度、灵敏度和f1评分分别为96.5%、98.83%、96.44%和96.4%。此外,层次的见解被检查,以获得决策过程的更深层次的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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