Authors :
Dr. P. Manikandaprabhu; S.S.Subaash
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/mvxj3p3j
Scribd :
https://tinyurl.com/34u8rdya
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1824
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Medical image classification plays a vital part
in identifying and detecting diseases. Vision impairment
affects 2.2 billion individuals globally, with cataracts,
glaucoma, and diabetic retinopathy as major
contributors. Timely diagnosis, crucial for effective
treatment, often relies on imaging like color fundus
photography. This study tackles multi-class classification
challenges in retinal diseases using MobileNetV2.
Traditional CNN models struggle with accuracy and
efficiency, prompting the exploration of lightweight
architectures. Leveraging MobileNetV2's efficiency, the
aim is to improve diagnosis using a comprehensive
ocular disease dataset. By integrating deep learning with
conventional methods, growing challenges in
ophthalmological analysis are addressed. The research
underscores the importance of collaborative efforts in
dataset curation, architecture design, and model
interpretability to advance the multi-class classification
of retinal diseases.
Keywords :
Color Fundus Photography, Classification, Deep Learning, Diabetic Retinopathy, Medical Image Processing, Multi-Class Classification.
Medical image classification plays a vital part
in identifying and detecting diseases. Vision impairment
affects 2.2 billion individuals globally, with cataracts,
glaucoma, and diabetic retinopathy as major
contributors. Timely diagnosis, crucial for effective
treatment, often relies on imaging like color fundus
photography. This study tackles multi-class classification
challenges in retinal diseases using MobileNetV2.
Traditional CNN models struggle with accuracy and
efficiency, prompting the exploration of lightweight
architectures. Leveraging MobileNetV2's efficiency, the
aim is to improve diagnosis using a comprehensive
ocular disease dataset. By integrating deep learning with
conventional methods, growing challenges in
ophthalmological analysis are addressed. The research
underscores the importance of collaborative efforts in
dataset curation, architecture design, and model
interpretability to advance the multi-class classification
of retinal diseases.
Keywords :
Color Fundus Photography, Classification, Deep Learning, Diabetic Retinopathy, Medical Image Processing, Multi-Class Classification.
