Abstract
Breast cancer is the leading type of cancer among women. When early detected, the chances of cure may reach up to 95%. Recent advances in screening techniques allow the detection of breast tumors even in millimetric sizes. Although precise, the analysis and interpretation of such medical images require a certain skill to avoid false negatives, therefore computer-aided diagnosis tools may assist doctors in detecting even subtle findings. This paper addresses the development and evaluation of a classification method to assist breast cancer diagnosis using infrared images. To do so, a Radiomic-based approach was used to represent the image’s content on a numerical feature vector which is later used to train a Fully-Connected Neural Network. Experiments following the K-Fold Cross-Validation protocol show that the Deep Neural Network achieved an overall accuracy of 98.61% and an AUC of 99.14%. Due to its high predictive rates, the proposed method showed itself very promising in aiding early breast cancer diagnosis using infrared imaging.
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Acknowledgments
M.F.O.B. is supported in part by Coordination for the Improvement of Higher Education Personnel (CAPES) under grant 88887.498626/2020-00. A.C. is supported in part by the National Institutes of Science and Technology (INCT - MACC project), National Council for Scientific and Technological (CNPq) under grant 402988/2016-7 e 305416/2018-9, the Research Support Foundation of Rio de Janeiro State (FAPERJ) over CNE, SIADE-2, e-Health Rio and Digit3D projects, and NVIDIA Research Grant for Anatomical Structure Segmentations Project.
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Baffa, M.d.F.O., Conci, A. (2022). Radiomics for Breast IR-Imaging Classification. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_2
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