Enhanced Medical Image Classification Using LSA and PCA in CNN

¹ PG Scholar, Department of CSE, Mohamed Sathak Engineering College, Kilakarai, India
² Assistant Professor, Department of CSE, Mohamed Sathak Engineering College, Kilakarai, India
³ Professor, Department of CSE(AIML), Vardhaman College of Engineering, Hyderabad, India
⁴ Professor, Department of CSE, Mohamed Sathak Engineering College, Kilakarai, India
⁵ Professor, Department of CSBS, Mohamed Sathak Engineering College, Kilakarai, India

^* Corresponding author: karthikhonda77@gmail.com

Abstract

We are presently living in the era where in medical field, the use of technology plays a major role in disease diagnosis and in treatment. In recent years Medical Image Processing play a significant role in modern diagnostics, where precision and accuracy are of highly important for planning and treatment of diseases. In this study, we present an enhanced approach that integrates Least Squares (LSA) alongside with Principal Component Analysis (PCA) within the Convolutional Neural Network (CNN) framework of deep learning to improve image processing and image resolution for medical diagnostics .Here LSA is employed to reduce the noise to the greater extent and to refine the feature for better clarity, while PCA employed in dimensionality reduction for efficient processing and preserving critical image details and at the same time CNN enables the automatic feature extraction and interpretation of image. Our results demonstrate that this combined LSA and PCA in CNN model offers significant improvement in image processing speed, efficiency in computation, reduction in noise present in the medical image, increasing sharpness of the image for high resolution leads to the accuracy in detection of diseases making it a promising method for advanced and enhanced medical imaging applications.