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  2. Vol 8, No 6 (2024)
  3. Armoogum

Breast Cancer Prediction Using Transfer Learning-Based Classification Model

Sheeba Armoogum, Kezhilen Motean, Deshinta Arrova Dewi, Tri Basuki Kurniawan, Jureerat Kijsomporn

Abstract


Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics.

 

Doi: 10.28991/ESJ-2024-08-06-014

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Keywords


Breast Cancer; Machine Learning; Deep Learning; Transfer Learning; CNN; BUSI; Process Innovation; Public Health.

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DOI: 10.28991/ESJ-2024-08-06-014

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