Abstract
Breast cancer is one of the fatal health conditions across globe and early detection of such cancerous tumor is life saver. There are various diagnostic ways to detect the tumor, however, ultrasound is more helpful for certain scenarios such as young patient, lactating or pregnant women, radiation sickness and biopsy assistance. This work attempts to detect breast tumors from the ultrasound images using Involuted UNET (InvUNET). It is based on the hybrid combination of deep learning concepts such as UNET and involution layer. UNET has been a popular choice in medical segmentation and the lightweight involution kernels embed the location-specific and channel-agnostic representation learning. The proposed method is validated using Breast Ultrasound Images (BUSI) dataset and jaccard score of 0.7146 is obtained.
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Chavan, T., Prajapati, K., JV, K.R. (2022). InvUNET: Involuted UNET for Breast Tumor Segmentation from Ultrasound. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_27
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DOI: https://doi.org/10.1007/978-3-031-09342-5_27
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