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Nur Rachmat
Poltekkes Kemenkes Surakarta
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Journal : Bulletin of Computer Science Research

 1 
Perbandingan Kinerja Arsitektur MobileneTV2 dan MobileneTV3 Dalam Klasifikasi Penyakit Retina pada Citra Optical Coherence Tomography (OCT) Menggunakan Optimizer AdamW dan SGD Ricko Andreas Kartono; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.915

Abstract

Retinal diseases are serious visual disorders that can lead to decreased visual function and even blindness. The diagnosis of retinal diseases is generally still performed manually by medical professionals through the examination of Optical Coherence Tomography (OCT) images, a process that requires considerable time, high precision, and is prone to diagnostic errors. Previous studies have mostly employed larger and more complex CNN architectures, with optimization limited to a few commonly used optimizers. This study aims to develop an automatic retinal disease classification model using Convolutional Neural Network (CNN) methods by leveraging the lightweight and efficient MobileNetV2 and MobileNetV3 architectures, enabling faster applications that can be deployed on resource-constrained devices. The architectures evaluated include MobileNetV2, MobileNetV3-Large, and MobileNetV3-Small, along with a comparison of two optimizers, namely AdamW and Stochastic Gradient Descent (SGD). The dataset used consists of 4,000 OCT images divided into four classes: Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen. The training process was conducted using a transfer learning approach, and model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the combination of the MobileNetV2 architecture with a batch size of 16 and either the AdamW or SGD optimizer achieved the best performance, reaching an accuracy of 85.75%, which is the highest among all tested configurations. These findings highlight the strong potential of lightweight architectures to be developed into fast, accurate, and field-deployable retinal disease diagnostic applications on mobile devices using deep learning.
Co-Authors Agus Seto Nugroho Ahmad Hisyam Aji Janmo Minulyo Aji, Sherly Ratih Frichesyarius Santi Alfan Zubaidi Alfan Zubaidi ANDYARINI, ESTI NOVI Anik Indah Yani Anik Indah Yani Anissa Eka Septiani Annisa Eka Septiani Ardi al Ghifari Ardi, Ardi al Ghifari Ardiansyah, Aldi Atika Febri Anggriani Ayuningtyas, Roro Aji Bambang Kuncoro Bambang Kuncoro Bambang Kuncoro Bambang Kuncoro Bee, Vanness Bella Permata Sihombing Permata , Mecha Caroline, Fionna Devi Elvina Rachma Doddy Suprayogi Dodiet Aditya Setyawan Dola Fitritha Raras Handayani Dwi Nurul Izzhati Dwi Setyawan Dwi Setyawan E. Saputra Saputra Esa Ridho Sambada Eviana S. Tambunan Fadhila Firmanurulita Fajar Susanti Faried Effendi Surono Fitri Khoirun Nisa haidar abdurrahman prawira Handayani, Dola Fitritha Raras Hanifah Hanifah Hanna Lestari Herawati Prianggi Herawati Priangi Indri Kusuma Dewi, Indri Kusuma Ismi Dwi Syafitri Izha Mahendra Johannes Petrus Jusuf Kristianto M Syafii M.Kurniawan Maharani Nadia Andarini Mardiani Mardiani Masdeniati, Masdeniati Muhammad Naufal Anugrah Muhammad Syafii Muhammad Syaifudin Mulyaningrum, Haryanti Katini Nella Tri Surya Ni Made Riasmini Noorma, Nilam Pibriana, Desi Prasetya, Hanung Prasetyo Catur Utomo Prasetyo Catur Utomo Prasetyo Catur Utomo Prianggi, Herawati Putra, Aji Putri Utami Sulistyawati R. Ismail Ismail Rachma, Devi Elvina REZA FAHLEVI Ricko Andreas Kartono Rini Tri Hastuti Rizi, Muhammad Alfa Rustam Aji, Rustam Setyorini, Yuyun Setyorini Siska Meiwijayasmi Sisybania Sri Djuwitaningsih Subagiyo, Didik SULISTIYANI SULISTIYANI Surya, Nella Tri Suryaningsih, Anthik Fajar Tri Handayani Wibowo, Suluh Arif Zenitha Bela Pratiwi Kusumawati