Unveiling PCOS Diagnosis with AI: A Comparative Approach using Machine Learning and Deep Learning
Keywords:
Machine Learning, Polycystic Ovary Syndrome, Segmentation, Transformers, Explainable AIAbstract
Polycystic Ovary Syndrome is a prevalent hormonal disorder among women of reproductive age, often resulting in irregular menstrual cycles, infertility, metabolic complications, and an increased risk of type 2 diabetes and Endometrial Cancer. In the realm of Computer-Aided Diagnosis, Machine Learning (ML) and Artificial Intelligence (AI) have become increasingly popular for tasks such as classification, prediction, and clustering, surpassing traditional methods in managing complex healthcare data. Numerous algorithms for machine learning, including Random Forest, Naive Bayes, Decision Trees, Multi-layer Perceptron’s, and Support Vector Machines, have been employed to categorize PCOS patients. Models for deep learning, including convolutional neural networks. Deep learning models, such as Convolutional Neural Networks, UNet, and Transformers, have advanced the field further. These models are used for image analysis and segmentation, and they achieve accuracy levels comparable to those of human experts. Explainable AI approaches are covered, along with segmentation and classification methods, for comprehending, interpreting, and assessing model predictions. Future directions and limitations in this field of study are still being investigated. This all inclusive approach leverages the benefits of numerous machine learning models to produce, improve, and analyze data, which eventually results in more accurate diagnostic instruments and dependable clinical practice outcomes.
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