Residual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer's disease severity detection
Yükleniyor...
Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PeerJ Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Background. Alzheimer's disease (AD) is a disease that manifests itself with a deteriora-tion in all mental activities, daily activities, and behaviors, especially memory, due to the constantly increasing damage to some parts of the brain as people age. Detecting AD at an early stage is a significant challenge. Various diagnostic devices are used to diagnose AD. Magnetic Resonance Images (MRI) devices are widely used to analyze and classify the stages of AD. However, the time-consuming process of recording the affected areas of the brain in the images obtained from these devices is another challenge. Therefore, conventional techniques cannot detect the early stage of AD.Methods. In this study, we proposed a deep learning model supported by a fusion loss model that includes fully connected layers and residual blocks to solve the above -mentioned challenges. The proposed model has been trained and tested on the publicly available T1-weighted MRI-based KAGGLE dataset. Data augmentation techniques were used after various preliminary operations were applied to the data set.Results. The proposed model effectively classified four AD classes in the KAGGLE dataset. The proposed model reached the test accuracy of 0.973 in binary classification and 0.982 in multi-class classification thanks to experimental studies and provided a superior classification performance than other studies in the literature. The proposed method can be used online to detect AD and has the feature of a system that will help doctors in the decision-making process.
Açıklama
This work was supported by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through project number (IF-PSAU-2021/01/18563) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Anahtar Kelimeler
Alzheimer's Disease, Categorical Generalized Focal Dice Loss, Deep Learning, New Hybrid Models, Classification
Kaynak
PeerJ Computer Science
WoS Q Değeri
Q1
Scopus Q Değeri
Q2
Cilt
9
Sayı
Künye
Alhudhaif, A., & Polat, K. (2023). Residual block fully connected DCNN with categorical generalized focal dice loss and its application to Alzheimer’s disease severity detection. PeerJ Computer Science, 9, e1599.
