Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Sarang Sharma; Sheifali Gupta; Deepali Gupta; Sapna Juneja; Amena Mahmoud; Shaker El-Sappagh; Kyung-Sup Kwak

doi:10.3389/fncom.2022.1000435

Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Front Comput Neurosci. 2022 Nov 1:16:1000435. doi: 10.3389/fncom.2022.1000435. eCollection 2022.

Authors

Sarang Sharma¹, Sheifali Gupta¹, Deepali Gupta¹, Sapna Juneja², Amena Mahmoud³, Shaker El-Sappagh^{4

5}, Kyung-Sup Kwak⁶

Affiliations

¹ Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chandigarh, Punjab, India.
² Department of Computer Science, KIET Group of Institutions, Ghaziabad, India.
³ Department of Computer Science, Kafrelsheikh University, Kafr el-Sheikh, Egypt.
⁴ Faculty of Computer Science and Engineering, Galala University, Suez, Egypt.
⁵ Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt.
⁶ Department of Information and Communication Engineering, Inha University, Incheon, South Korea.

Abstract

Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.

Keywords: Alzheimer's disease; classification; confusion matrix; feature visualization; modified inception.