Journal
ICACSIS 2020: 2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS)
Volume -, Issue -, Pages 77-81Publisher
IEEE
DOI: 10.1109/icacsis51025.2020.9263234
Keywords
Composition characteristics; deep learning; LeNet; synthetic minority over-sampling technique (SMOTE); thyroid nodules
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There are ten characteristics of ultrasound thyroid nodules, and each one has its categories. One particular characteristic is composition characteristic, as the occurrence of its categories can be one of the lead to the incidence of malignancy. This study developed a method to help experts identifying the categories of the composition ultrasound characteristic using data collected from the Department of Radiology RSUP Dr. Sardjito. This dataset was already cropped by the experts, leaving only the thyroid nodule area as the region of interest. The dataset of ultrasound images was going to pre-processing first to remove the labels, markers, and unnecessary artifacts. To further remove any unnecessary artifacts, the pre-processed image was segmented. Afterward, the data augmentation begins using the synthetic minority over-sampling technique (SMOTE). The augmentation result was sent to LeNet to be classified into three categories those are cystic, solid, and complex. The testing result outperformed previous studies with 92% accuracy, 85.71% sensitivity, 92.50% specfficity, 93.5S% PPV, 95.09% NPV, 0.918 F Score, and 146. 4s testing time.
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