Work place: Pillai College of Engineering, New Panvel, India
E-mail: schukka20it@student.mes.ac.in
Website: https://orcid.org/0009-0003-1784-9617
Research Interests:
Biography
Sahil Chukka has recently graduated with a degree in Bachelors of Technology(B.Tech) in Information Technology, specializing in Artificial Intelligence and Machine Learning from Pillai College of Engineering, situated in New Panvel, 410206. His academic journey represents a deep passion and enthusiasm for new age technology with particular interests in the field of Artificial Intelligence(AI), Machine Learning(ML) and Deep Learning(DL).
By Sahil Chukka Vardhanika Jagtap Naveen Patel Sudiksha Jadhav Mimi Cherian Jinesh Melvin Y. I.
DOI: https://doi.org/10.5815/ijigsp.2025.04.07, Pub. Date: 8 Aug. 2025
In ophthalmology, Choroidal Neovascularization (CNV) is a serious medical disease that, if left untreated, frequently results in significant vision loss. In this investigation, we investigate the evaluation and working of deep learning models, notably basic Convolutional Neural Networks (CNN), ResNet18, ResNet50, VGG16, VGG19, Vision Transformers, EfficientNetV2L, MobileNetV2 and InceptionV3 for identification and classification of CNV in Optical Coherence Tomography (OCT) images. The Kermany dataset, which includes OCT images of both CNV-patients and non-CNV patients (Normal OCT images) are utilized for this paper. The dataset was further used in three different versions based on validation and training split. The images from the dataset are already pre-processed and labelled so no pre-processing operations were performed, how- ever resizing of images have been performed according to the models. The deep learning models are trained and evaluated on standard performance metrics such as precision, recall, accuracy, F1-score, etc. All things considered, our work shows the evaluation of deep learning models to classify OCT images that show the presence of CNV. Based on all three dataset versions, the findings of our study confirm that ResNet18, VGGNet19, and MobileNetV2 beat all other approaches and achieved an average accuracy of 1. Additionally, Vision Transformer and Effi- cientNetV2L demonstrated strong performance, averaging 0.99 and 0.96 accuracy on each of the three dataset versions, respectively. These models have the potential to help ophthalmologists detect CNV early and monitor it, which may lead to prompt treatment and better vision preservation for patients.
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