• Mr Manjunatha C Parameshwarappa
• Dr Jithendra P R Nayak

Retinitis pigmentosa (RP) is a progressive inherited retinal disorder primarilly due to the degeneration of rod and cone cells, leading to loss of vision as the time passes and may lead to legal blindness. As early diagnosis plays a crucial role in patient care and treatment planning, there has been growing interest in leveraging deep learning based techniques to improve the accuracy and efficiency of RP detection. This paper presents a comprehensive review of the current deep learning-based approaches developed for identifying RP using ophthalmic imaging modalities, such as fundus photographs and optical coherence tomography. We review frequently used neural network architectures, including convolutional neural networks (CNNs), as well as emerging techniques like transfer learning. In addition to discussing the datasets employed and performance benchmarks reported, this article also addresses key challenges such as data scarcity, model interpretability. This paper aims to provide researchers and clinicians with a clear understanding of the current landscape and future prospects of deep learning in the automated detection of retinitis pigmentosa.

Deep learning, Machine learning, Retinitis pigmentosa, Image processing

"A COMPREHENSIVE REVIEW OF DEEP LEARNING BASED TECHNIQUES FOR THE RETINITIS PIGMENTOSA DETECTION: A COMMON INHERITED RETINAL DISORDER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.897-903, July-2025, Available :http://www.jetir.org/papers/JETIRGX06169.pdf

"A COMPREHENSIVE REVIEW OF DEEP LEARNING BASED TECHNIQUES FOR THE RETINITIS PIGMENTOSA DETECTION: A COMMON INHERITED RETINAL DISORDER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp897-903, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06169.pdf