Classification of Breast Cancer Based on Ultrasound Image Using CNN Transfer Learning Method

Irawati, Putri Anna Diah (2022) Classification of Breast Cancer Based on Ultrasound Image Using CNN Transfer Learning Method. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Breast cancer is a deadly cancer that most often affects women. According to an article released by the World Health Organization, (2021) nearly 2.3 million women were diagnosed with breast cancer with 685 thousand deaths. Patients who have symptoms of breast cancer or feel an abnormality in the breast will need to undergo tests including breast imaging and in some cases tissue sampling (biopsy). There are many imaging techniques for breast cancer, and breast ultrasound is one of them, which can provide quite useful and comprehensive information. The use of deep learning to classify types of breast cancer is also widely carried out and transfer learning methods are quite popular in the classification of the medical field. In this study, a desktop-based application is created for the classification of breast cancer types into malignant, benign, and normal classes using transfer learning methods from the pretrained Alexnet network. The experiment was carried out eight times with a combination of three variables, namely the image preprocessing method, the training data distribution method, and the classification method. Evaluation is done by analyzing the confusion matrix and paying attention to the accuracy values, Positive Predictive Value (PPV), and True Positive Rate (TPR). The conclusions obtained from these eight experiments, obtained the best combination of variables for learning, namely by preprocessing the dataset, distributing the dataset using K-fold, and classification using Fully connected layer. After the model is implemented into the application, the overall average evaluation results for PPV = 92.3%, TPR = 86.7%, and Accuracy = 90%. Keywords: Transfer Learning, Breast Cancer, Alexnet

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