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A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification

By
G. Meenalochini ,
G. Meenalochini

School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu

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D. Amutha Guka ,
D. Amutha Guka

School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu

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Ramkumar Sivasakthivel ,
Ramkumar Sivasakthivel

Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India

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Manikandan Rajagopal ,
Manikandan Rajagopal

Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India

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Abstract

According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8%. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy

 

How to Cite

1.
Meenalochini G, Guka DA, Sivasakthivel R, Rajagopal M. A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification. Data and Metadata [Internet]. 2024 Feb. 7 [cited 2024 Apr. 24];3:198. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/198

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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