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Journal : Infact: Jurnal Sains dan Komputer

Implementation of Convolutional Neural Network for Detecting Cataract Disease Severity in Eye Images Fadlilatunnisa, Fanny; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.712

Abstract

Cataract is a condition that causes clouding of the lens of the eye, leading to blindness and poor vision. According to the WHO, around 18 million people suffer from cataract-related blindness, making it one of the leading causes of blindness globally. Prompt and accurate diagnosis is essential to prevent more serious outcomes. This research aims to develop a deep learning model that utilises Convolutional Neural Networks (CNN) in categorising cataract severity into four groups: hypermature, normal, immature and mature. This model is expected to provide a more efficient and accurate alternative to traditional methods in diagnosing cataracts. To achieve this, we implemented transfer learning using three popular CNN architectures: VGG16, VGG19, and ResNet50. Experiments were conducted using a dataset of pre-labelled eye images for training. Model performance was evaluated by calculating F1-score, recall, accuracy, and precision using a confusion matrix. The results showed that VGG19 produced 88% accuracy and F1-score of 0.87, while VGG16 had the best accuracy. On the other hand, ResNet50 showed the lowest accuracy with 63% and F1-score of 0.59. These findings highlight the importance of selecting the right CNN architecture for cataract diagnosis, while underlining the potential application of deep learning in ophthalmology.
Enhanced Dermatological Diagnosis: Autoimmune and Non-Autoimmune Skin Disease Classification Using MobileNet and ResNet Tyara Regina Nadya Putri; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.711

Abstract

Autoimmune diseases arise when the immune system mistakenly attacks the body's healthy cells, causing a range of symptoms that can greatly affect a patient's quality of life. In Indonesia, these conditions present a significant public health concern. According to research by Ministry of Health Republic Indonesia in 2024, autoimmune lupus affects approximately 0.5% of the population, impacting over 1.3 million individuals. This study proposes a classification and detection model utilizing Convolutional Neural Networks (CNN) with transfer learning, incorporating MobileNetV2, MobileNetV3Small, MobileNetV3Large, ResNet50, ResNet101, and ResNet152 architectures. The model's performance is assessed using a confusion matrix, evaluating precision, recall, and F1-score, while computational efficiency is analyzed using a GPU T4. Experimental results demonstrate that ResNet152 achieved the highest accuracy at 92%. These findings emphasize the crucial role of selecting an optimal CNN architecture to enhance the accuracy of autoimmune and non-autoimmune skin disease classification and detection.
Co-Authors Achmad Fansuri Achmad Randhy Hans Adhi Fernandes Gamaliel Adhikara, M. F. Arrozi Adilah Widiasti Ahmad Musnansyah Ahmad Mutedi Akbar , Habibullah Akbar, Habibullah Alivia Yufitri Andriana, Dian Annazma Ghazalba Ari Widatama, Yohanes Bagas Arif Pami Setiaji Bambang Irawan Bambang Irawan Bambang Irawan Bayu Sulistiyanto Ipung Sutejo Binastya Anggara Sekti Budi Aribowo BUDI TJAHJONO Budi Tjahjono Budi Tjahjono Budi Tjahyono Budi Tjahyono Budi Tjahyono Cahya Darmarjati Deni Iskandar Deni Iskandar Dewi, Riris Septiana Sita Doni Antoro Dulbahri Dulbahri Dwiaji, Lingga Eko Prasetyo Endang Ruswanti Endang Ruswanti Erry Yudhya Mulyani Erry Yudhya Mulyani Erry Yudhya Mulyani Euis Heryati Fadlilatunnisa, Fanny Fatonah, Nenden Siti Fikri Saefullah Firmansyah, Gerry Gerry Firmansyah Gerry Firmansyah Gerry Firmasyah Gusti Fachman Pramudi Habibullah Akbar Hadi, Muhammad Abdullah Hadjarati, Panji Ramadhan Yudha Putra Hani Dewi Ariessanti Hartono Hartono Haryoto, Iin Sahuri Hendaryatna Hendaryatna Heri Wijayanto I Gede Pasek Suta Wijaya Ichwani, Arief Ichwani, arief Ipung Sutejo, Bayu Sulistiyanto Ismiyati Meiharsiwi Iwan Setiawan Izhar Rahim Joniwan Joniwan Karisma Trinanda Putra Kartini Kartini Krisogonus Wiero Baba Kaju Kundang Karsono Juman Kundang Karsono Juman Kundang Karsono Juman Kus Hendrawan Muiz Lingga Dwiaji Lisdiana Lisdiana Martin Saputra, Martin Massie, Julius Ivander Maulana, Syaban Meria, Lista Muhamad Bahrul Ulum Muhamad Bahrul Ulum Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Muttaqin, Naufal Hafizh Nainggolan, Restamauli br Nina Nurhasanah Nindyo Artha Dewantara Wardhana Nixon Erzed Nixon Erzed Nizirwan Anwar Nizirwan Anwar Nurfilael, Gagas Nurfilae Nurhayati, Ety Pratama, Fajar Prayitno Purwano SK Rahaman, Mosiur Rian Adi Pamungkas Rifqi Khairurrahman RILLA GANTINO Rizki Faro Khatiningsih Rizky Aulia Roesfiansjah Rasjidin Sholeh Gunawan Simorangkir, Holder Suardana, Made Aka Sularso Budilaksono Sulistyo, Catur Agus Sunardi, Sunardi Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu Tjahjono, Budi Tyara Regina Nadya Putri Ulum, Muhamad Bahrul Ummanah Ummanah, Ummanah Vitri Tundjungsari Wahid Abdul Azis Wardhana, Nindyo Artha Dewantara Widiasti, Adilah William Nugraha Wisnujati, Andika Yanathifal Salsabila Anggraeni Yessy Oktafriani Yulhendri Yulhendri