Santoresh Kumari Dhimann

Effect of Degradations Introduced by Imaging Systems in Patch-Based Machine Learning for Mammogram Cancer Detection

Santoresh Kumari Dhimann, Rakesh Kumar Yadav

Machine learning-based breast cancer detection from mammograms is inherently affected by image degradation induced by imaging systems. Conventional algorithms mainly target enhancement of classification performance in an ideal imaging scenario; little consideration is generally given to the impact of imaging degradations on …

Patch-Based Mammogram Cancer Detection Using Attention-Driven CNNs

Santoresh Kumari Dhimann, Rakesh Kumar Yadav

The research introduces a novel breast cancer (BC) detection model that integrates Convolutional Neural Networks (CNNs) with a circular attention mechanism to improve diagnostic accuracy in mammograms. By employing a patch-based approach, mammograms are segmented into 50 × 50 pixel patches, allowing …

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Deep Learning-Driven Pectoral Muscle Detection and Elimination in Digital Mammograms Using U-Net Architecture

Santoresh Kumari Dhimann, Rakesh Kumar Yadav

Precise detection of the pectoral muscle in mediolateral oblique (MLO) mammograms is vital for the reliability of automated breast cancer detection systems. As a result, the bright, triangular morphology of the pectoral muscle is often indistinguishable from a malignant lesion, thereby contributing …

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection

Santoresh Kumari Dhimann, Tinny Sawhney, Rakesh Kumar Yadav

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The …

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