Cataract and glaucoma detection based on Transfer Learning using MobileNet
Sheikh Muhammad Saqib,
Muhammad Iqbal,
Muhammad Zubair Asghar,
Tehseen Mazhar,
Ahmad Almogren,
Ateeq Ur Rehman,
Habib Hamam
Affiliations
Sheikh Muhammad Saqib
Department of Computing and Information Technology, Gomal University, D.I.Khan 29050, Pakistan
Muhammad Iqbal
Department of Computing and Information Technology, Gomal University, D.I.Khan 29050, Pakistan; Gomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal University, D.I. Khan 29050, Pakistan; Department of Computer Science, Virtual University of Pakistan, Lahore, 51000, Pakistan; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia; School of Computing, Gachon University, Seongnam, 13120, Republic of Korea; Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada; School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa; Hodmas University College, Taleh Area, Mogadishu, Somalia; Bridges for Academic Excellence, Tunis, Tunisia
Muhammad Zubair Asghar
Department of Computing and Information Technology, Gomal University, D.I.Khan 29050, Pakistan; Gomal Research Institute of Computing (GRIC), Faculty of Computing, Gomal University, D.I. Khan 29050, Pakistan; Department of Computer Science, Virtual University of Pakistan, Lahore, 51000, Pakistan; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia; School of Computing, Gachon University, Seongnam, 13120, Republic of Korea; Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada; School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa; Hodmas University College, Taleh Area, Mogadishu, Somalia; Bridges for Academic Excellence, Tunis, Tunisia
Tehseen Mazhar
Department of Computer Science, Virtual University of Pakistan, Lahore, 51000, Pakistan; Corresponding author.
Ahmad Almogren
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia
Ateeq Ur Rehman
School of Computing, Gachon University, Seongnam, 13120, Republic of Korea; Corresponding author.
Habib Hamam
Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada; School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa; Hodmas University College, Taleh Area, Mogadishu, Somalia; Bridges for Academic Excellence, Tunis, Tunisia
A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.
