Alam, Md. AshrafulKarim, Dewan ZiaulKhan, Farhan SakibFerdaus, NowshinHossain, TamimIslam, Quazi SabrinaIslam, Md. Iftakharul2022-11-152022-11-1520222022-05ID 18301176ID 18101113ID 18301183ID 19101673ID 18301020http://hdl.handle.net/10361/17570Cataloged from PDF version of thesis.Includes bibliographical references (pages 33-36).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Optical Coherence Tomography (OCT) is an effective approach for diagnosing retinal problems that can be used in combination with traditional diagnostic testing methods. We developed and implemented a deep Convolutional Neural Network (CNN) model, which has the capability to effectively identify and classify Optical Coherence Tomography (OCT) images into the following four categories: Normal, DMD, CNV, and DME. The proposed 21 layered CNN model is built with three basic layers: a convolutional layer, a pooling layer, and a fully connected layer along with dropout and dense layers. Our proposed model is able to detect and differentiate between the OCT images with a high amount of accuracy. The 21 layer proposed CNN model was used for the classification and diagnosis of retinal sickness using OCT images. To justify the efficiency of our custom CNN model, seven pre-trained CNN models (VGG16, VGG19, MobNetV2, Resnet50, DenseNet121, InceptionV3, and InceptionResNetV2) were used and testified with the same amount of dataset. In terms of the accuracy, precision, recall, and f1 score, which are all tested in this paper, the suggested CNN model along with seven other pre-trained CNN architectures perform comparable on the available dataset. The proposed model has an accuracy rate of 98.37 percent, which is greater than any of the experimental results of the CNN models utilized in this research due to the fact that the recommended model was developed. When it comes to the diagnosis of retinal problems, the CNN model that was suggested performs far better than any other model that was previously used.36 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Convolutional neural networkOptical coherence tomographyDeep learningRetinal DiseaseVGG16VGG19 MobNetV2ResNet50DenseNet121InceptionV3InceptionResNetV2Neural networks (Computer science)Optical coherence tomographyThesis