Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models

Nasya Amirah Melyani; Ayuni Fachrunisa Lubis; Aghnia Tatamara; Ryando Rama Haiban; Muhammad Iltizam; Muhammad Aufi Rofiqi; Sakhi Hasan Abdurrahman; Nitasnim Samae; Bilal Shahid; Muhammad Habibullah; Muhammad Ibrara Ismail

doi:10.57152/predatecs.v3i1.1559

Authors

Nasya Amirah Melyani Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Ayuni Fachrunisa Lubis Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Aghnia Tatamara Dokuz Eylül Üniversitesi, Turkey
Ryando Rama Haiban Yarmouk University, Jordan
Muhammad Iltizam Universiti Pendidikan Sultan Idris, Malaysia
Muhammad Aufi Rofiqi Al-Azhar University, Egypt
Sakhi Hasan Abdurrahman Al-Azhar University, Egypt
Nitasnim Samae Prince of Songkla University, Thailand
Bilal Shahid Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Pakistan
Muhammad Habibullah Al-Wasatiyah University, Yamen
Muhammad Ibrara Ismail Al-Wasatiyah University, Yamen

DOI:

https://doi.org/10.57152/predatecs.v3i1.1559

Keywords:

Cataract, Convolutional Neural Network, Diabetic Retinophaty, Eye Fundus, Glaukoma

Abstract

Eye disease is a significant global health problem, with more than two billion people experiencing vision impairment. Some of the main causes of visual impairment include cataracts, glaucoma, diabetic retinopathy, and age-related macular degeneration. Early detection of eye disease is very important to prevent blindness. The fundus of the eye, which includes the retina and blood vessels, is an important area in the diagnosis of retinal diseases. Fundus disease can cause significant vision loss and is one of the leading causes of blindness. Automated analysis of fundus images is used to diagnose common retinal diseases, ranging from easily treatable to very complex conditions. This research discusses eye disease image classification using several Convolutional Neural Network (CNN) architectures, namely Inception V3, DenseNet 121, and MobileNet V2. The dataset used is 4217 fundus images categorized based on the patient's health condition. Data is processed through normalization and augmentation to improve model performance. Experimental results show that MobileNet V2 has the highest accuracy of 81.3%, followed by Inception V3 with 77.3%, and DenseNet 121 with 76.7%. The use of appropriate CNN models in the classification of eye fundus images can help in early detection of eye diseases, thereby preventing further visual impairment.

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