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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Informatics and Computation
Putra Wanda
Universitas Respati Yogyakarta
Computer Science & IT

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EFFECTIVE BREAST CANCER DETECTION USING NOVEL DEEP LEARNING ALGORITHM Irawadi Buyung; Agus Qomaruddin Munir; Putra Wanda
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1363.386 KB) | DOI: 10.33480/jitk.v8i2.4077

Abstract

Ultrasound is one of the most common screening tools for breast cancer detection. However, the lack of qualified radiologists causes the diagnosis process to become a challenging task. Deep learning's promising achievement in various computer vision problems inspires us to apply the technology to medical image recognition problems. We propose a detection model based on the Rapid-CNN to detect breast cancer quickly and accurately. We conduct this experiment by collecting breast cancer datasets, pre-processing, training models, and evaluating the model performance. This model can detect breast cancer with bounding boxes based on the experiment result. In this model, it is possible to detect the bounding box that is more than what it should be, so we applied NMS to eliminate the prediction of the bounding box that is less precise to increase accuracy.
How to Stepping up Characters Recognition using CNN Algorithm? Al-Sadi Khaled; Putra Wanda
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.53

Abstract

Character recognition is very important to understand ancient culture. Various papers proposed numerous to deal with handwritten character recognition. However, several traditional remain drawbacks because methods still rely on operations based on visual capabilities. Therefore, to deal with the issue, we propose a novel recognition model using a Convolutional Neural Network to produce an effective result. To build the model, we collect datasets, do preprocessing, training with several different parameters to get the highest accuracy results. Based on experiments, our proposed model can produce an accuracy quality with a value of 98.00%.
Co-Authors Agus Qomaruddin Munir Al-Sadi Khaled Irawadi Buyung