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JCSIS

Cancer Diagnosis Optimization Using Deep Learning Algorithms and Neural Networks in Medical Imaging

Narcisa ZlatanHakan KhanNajaad OubeBlika
Volume 4

Abstract

The early and accurate diagnosis of cancer is critical for improving patient outcomes. This study explores the use of deep learning algorithms and neural networks to optimize cancer diagnosis through medical imaging techniques, such as MRI, CT scans, and X-rays. By leveraging advanced image processing methods and convolutional neural networks (CNNs), the model is trained to identify and classify malignant tumors with high accuracy. The neural network analyzes complex imaging data, extracting key features that are often difficult for human clinicians to detect. The system also integrates a feature selection process to enhance diagnostic precision by focusing on the most relevant patterns and characteristics in the medical images. The proposed approach aims to assist healthcare professionals in diagnosing cancer more efficiently and effectively, reducing the time required for diagnosis while maintaining high levels of accuracy. The results highlight the potential of deep learning in revolutionizing cancer diagnosis, offering a reliable tool for clinicians to enhance their decision-making process.

Keywords

Cancer diagnosis, deep learning, neural networks, medical imaging, tumor detection, convolutional neural networks

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