• NAGESH KUMAR KORADA
• SRUTHI HASINI NEERUKATTU
• ASHRITHA BHAVANI SATYAVARAPU
• GNANA SAI BANDI
• MURTHY V.S.V.S

As of today, doctors have to spend lot of time for analyzing the scanned Chest x-rays images. To avoid this, generating a system which can detect the abnormalities from scanned chest x-ray images and converting them into understandable description can be an efficient way to examine the reports within short span of time. The generated report will be verified by the doctor. This can help doctors to easily diagnose the medical report. In this project, we have implemented Image Captioning on Chest x-ray medical images using deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Prominent features are extracted from chest x-ray images using CHEXNET CNN model. These features are given as input to long short-term Memory framework to generate long descriptive captions for better understanding of internal medical problems faced by the patients.

Chest x-rays, Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), ChexNet, Image Captioning

"MEDICAL IMAGE CAPTIONING ON CHEST X-RAYS USING CHEXNET AND LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i661-i669, March-2024, Available :http://www.jetir.org/papers/JETIR2403885.pdf

"MEDICAL IMAGE CAPTIONING ON CHEST X-RAYS USING CHEXNET AND LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi661-i669, March-2024, Available at : http://www.jetir.org/papers/JETIR2403885.pdf