PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM KLASIFIKASI KANKER PAYUDARA BERDASARKAN CITRA USG
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Hepitia, Dila
Agtriadi, Herman Bedi
Kuswardani, Dwina
Agtriadi, Herman Bedi
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2023-06-21 01:56:09
Abstract :
Breast cancer is a condition in which abnormal cells grow in the breast tissue. Breast cancer usually grows in several parts, one of which is in the glands that produce milk (lobules). One way to check for breast cancer is by using ultrasound which is useful for seeing the inside of the breast whether there are signs of breast cancer that cannot be seen by the eye directly, but the results of the ultrasound do not always produce good results so that it can influence doctors to This is the goal of the researcher in conducting this research which implements the Convolutional Neural Network method on the system created, which aims to be a comparison between visual observations and computational observations in increasing accuracy in diagnosis in classification of ultrasound images that detect between normal, benign, malignant. In this study also use the Sharpen kernel in the convolution process because the sharpen kernel (Matrix 3x3) helps in sharpening the image. In conducting this research, the researcher used a dataset of 520. To get the results of testing the classification system that was created, the Confusion Matrix was used. And the results based on the training model process obtained an accuracy of 76% for the 20th epoch plan. By taking 30 samples of the data tested on the system, the accuracy of the system was 93%.
Breast cancer is a condition in which abnormal cells grow in the breast tissue. Breast cancer usually grows in several parts, one of which is in the glands that produce milk (lobules). One way to check for breast cancer is by using ultrasound which is useful for seeing the inside of the breast whether there are signs of breast cancer that cannot be seen by the eye directly, but the results of the ultrasound do not always produce good results so that it can influence doctors to This is the goal of the researcher in conducting this research which implements the Convolutional Neural Network method on the system created, which aims to be a comparison between visual observations and computational observations in increasing accuracy in diagnosis in classification of ultrasound images that detect between normal, benign, malignant. In this study also use the Sharpen kernel in the convolution process because the sharpen kernel (Matrix 3x3) helps in sharpening the image. In conducting this research, the researcher used a dataset of 520. To get the results of testing the classification system that was created, the Confusion Matrix was used. And the results based on the training model process obtained an accuracy of 76% for the 20th epoch plan. By taking 30 samples of the data tested on the system, the accuracy of the system was 93%.
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