Paper Key : IRJ************048
Author: Sonal Madhukar Ghuge,Jyotsna Chotulal Chitte,Sharvaree Shekhar Bamane
Date Published: 11 Apr 2024
Abstract
AbstractKidney diseases pose a significant health threat globally, often leading to severe complications if not diagnosed and treated early. In recent years, advancements in medical imaging and machine learning techniques have shown promising results in the early detection and classification of kidney abnor- malities. This study proposes a novel approach utilizing image processing techniques implemented in TensorFlow and Keras to accurately detect and classify kidney diseases into four classes: normal, cyst, tumor, and stone.The proposed system begins with the acquisition of kidney images through various medical imaging modalities such as ultrasound,xray. These images are pre-processed to enhance their quality and standardize features for improved analysis. Convolutional Neural Networks (CNN), a powerful class of deep learning models, are then employed to automatically extract discriminative features from the pre- processed images. TensorFlow and Keras frameworks are utilized for the development and training of the CNN models.Keywords: Kidney Disease Detection using Deep learning, Scanned Images,Image Processing,Confusion matrix, Predict Dis- ease, etc...
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