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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Eugine Prince M
Assistant Professor, Department of Physics, S.T.Hindu College, Nagercoil, Tamilnadu, India.
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification Felix Joseph X; Maithili Vijayakumar; Sujatha Therese P; Josephin Shermila P; Eugine Prince M; Maris Murugan T
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5938

Abstract

Both adults and children are at risk of dying from brain tumors. On the other hand, prompt and precise detection can save lives. Early detection is necessary for a proper diagnosis of a brain tumor, and magnetic resonance imaging (MRI) is often used in this context. To assist in the early diagnosis of sickness, neuro-oncologists have used Computer-Aided Diagnosis (CAD) in a number of ways. In this study, proposed a hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET)-based deep learning was developed to distinguish between cancers and non-tumors. Three processes make up this method: pre-processing, classification, and feature extraction. Pre-processing methods used in this study included contrast enhancement and image shrinking. The MRI picture is processed to get the wavelet and texture properties and used to build a classifier. Using MRI scans, the proposed hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model classifies two types of brain tumors: tumor and non-tumor. Performance criteria such as accuracy (ACC), specificity (SP), and sensitivity (SE) are used to assess the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model. Based on 850 images, the studies yielded a 99.54% categorization accuracy demonstrate that the suggested hybrid Depthwise-Direct Acyclic Graph Network (D-DAGNET) model beats the most advanced methods.
Co-Authors Felix Joseph X Josephin Shermila P Maithili Vijayakumar Maris Murugan T Sujatha Therese P