{"paper_id":"cb77ee4d-a694-4ab9-a9bc-0327ed809623","body_text":"Abstract\nThis study aims to improve the classification of adenomyosis, a medical condition characterized by the invasion of endometrium into the myometrium, using reinforcement learning (RL) and image segmentation. By applying RL, a form of machine learning, the uterine regions of women can be classified as either malignant or benign, thereby enhancing the detection accuracy of adenomyosis. Preprocessing procedures are conducted prior to the classification phase to ensure precise quantification of adenomyosis severity on MR scans. The proposed method utilizes fuzzy clustering and adaptive neighborhood range in RL to identify regions of interest in the uterus. Evaluation metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), and Dice coefficient (DC) are used to assess the effectiveness of the approach. The results demonstrate the potential of RL-based segmentation for improving the classification and detection of adenomyosis by providing 95% accuracy, offering implications for accurate diagnosis and treatment planning for affected women.\nAccess this chapter\nTax calculation will be finalised at checkout\nPurchases are for personal use only\nSimilar content being viewed by others\nReferences\nAmorim JGA, Macarini LAB, Matias AV, Cerentini A, Onofre FBDM, Onofre ASC, Von Wangenheim A (2020) A novel approach on segmentation of AgNOR-stained cytology images using deep learning. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS), pp 552–557\nJin Y, Huang W, Qu Q (2022) Analysis of convolutional neural network segmentation algorithm of adenomyoma. Comput Intell Neurosci CIN\nYang Z, Shu Y, Liu S, Cai C, Wu Z, Yin Z, Gong W (2022) Differential diagnosis of benign and malignant gallbladder lesions using CT based machine learning. 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Adenomyosis Segmentation Leveraging Reinforcement Learning Techniques. In: Bhateja, V., Abdul Hameed, V., Udgata, S.K., Azar, A.T. (eds) Innovations in Communication Networks: Sustainability for Societal and Industrial Impact. ICDECT 2024. Lecture Notes in Networks and Systems, vol 1365. Springer, Singapore. https://doi.org/10.1007/978-981-96-5223-5_8\nDownload citation\nDOI: https://doi.org/10.1007/978-981-96-5223-5_8\nPublished:\nPublisher Name: Springer, Singapore\nPrint ISBN: 978-981-96-5222-8\nOnline ISBN: 978-981-96-5223-5\neBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)Springer Nature Proceedings excluding Computer Science","source_license":"CC0","license_restricted":false}