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All Journal Jurnal Teknologi JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Emerging Science Journal Jurnal Varian Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal for Applied Information Management Maritime Park: Journal Of Maritime Technology and Socienty Bulletin of Applied Mathematics and Mathematics Education Jurnal Pemberdayaan Masyarakat Journal of Current Research in Blockchain International Journal Research on Metaverse
Dewi, Deshinta Arrova
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Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : Emerging Science Journal

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Breast Cancer Prediction Using Transfer Learning-Based Classification Model Armoogum, Sheeba; Motean, Kezhilen; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Kijsomporn, Jureerat
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-014

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 Full Text: PDF
Co-Authors - Kurniawan, - Achsan, Harry Tursulistyono Yani Adi Wijaya Afriyani, Sintia Alde Alanda, Alde Ali Amran Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Andriani, Putu Eka Anita Desiani Aris Thobirin, Aris Armoogum, Sheeba Armoogum, Vinaye Aryananda, Rangga Laksana Asro, Asro Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bin Abdul Hadi, Abdul Razak Bujang, Nurul Shaira Binti Chandra, Anurag Devi Udariansyah Diana Diana Dita Amelia, Dita Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Endro Setyo Cahyono, Endro Setyo Eva Yulia Puspaningrum Fachry Abda El Rahman Fadly Fadly Fara Disa Durry Fatoni, Fatoni Fikri, Ruki Rizal Nul Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Hasibuan, Muhammad Siad Henderi . Hendra Kurniawan Heng, Chang Ding Hidayani, Nieta Hisham, Putri Aisha Athira binti Humairah, Sayyidah Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Jayawarsa, A.A. Ketut Kezhilen, Motean Kijsomporn, Jureerat Kurniawan, Tri Basuki Larasati, Anggit Lexianingrum, Siti Rahayu Pratami Lin, Leong Chi M Said Hasibuan M. Fariz Fadillah Mardianto Maizary, Ary Mantena, Jeevana Sujitha MARIA BINTANG Mas Diyasa, I Gede Susrama Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Murnawan, Murnawan Nathan, Yogeswaran Nazmi, Che Mohd Alif Onn, Choo Wou Pamungkas, Anjar Periasamy, Jeyarani Pratiwi, Ananda Pratiwi, Firda Aulia Praveen, S Phani Putra, Muhammad Daffa Arviano Putrie, Andi Vania Ghalliyah R Rizal Isnanto Rahmadani, Olivia Samihardjo, Rosalim Saringat, Zainuri Setiawan, Ariyono Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Slamet Riyadi Sri Karnila Sri Lestari Sugiyarto Surono, Sugiyarto Sulaiman, Agus Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Trinawarman, Dedi Wahyu Caesarendra Wahyu Dwi Lestari Wahyuningdiah Trisari Harsanti Putri Wei, Aik Sam Wibaselppa, Anggawidia Widyangga, Pressylia Aluisina Putri Widyaningsih , Upik Wijayanti, Dian Eka Yeh, Ming-Lang Yorman Yuli Andriani Zakari, Mohd Zaki Zakaria, Mohd Zaki