Integration of Deep Learning Algorithms for Precision Cervical Cancer Analysis from Colposcopic Images
Keywords:
Cervical cancer detection, colposcopy images, Deep learning algorithms, Medical image analysisAbstract
The introduction of deep learning algorithms has significantly transformed the identification of cervical cancer, especially in the interpretation of colposcopy pictures. This study examines the incorporation and comparative effectiveness of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transfer Learning models for accurate analysis of cervical cancer, using the “IARC Cervical Cancer Image” dataset. The importance of promptly and precisely identifying cervical cancer, emphasizing the crucial function of colposcopy imaging in revealing cellular abnormalities. This text discusses the complexities of collecting and preparing data, with a specific emphasis on customized approaches such image rescaling, color normalization, and noise reduction. These techniques are used to provide the best possible adaption to the “IARC Cervical Cancer Image” dataset. The justification for choosing deep learning methods is explained, which sets the stage for a thorough comparison examination. The research provides a comprehensive analysis of CNN, RNN, and Transfer Learning models in many categories, encompassing normal cells, benign abnormalities, pre-cancer and cancer. The performance indicators, including accuracy, precision, recall, F1 score, and AUC-ROC, are provided to give a comprehensive understanding of the capabilities and constraints of each method. The paper explores the consequences of cervical cancer diagnosis and its broader impact on the area of medical imaging, providing insights for future research endeavors. This study enhances our knowledge of incorporating deep learning algorithms into precise analysis of cervical cancer, with a particular focus on the possible implications for clinical diagnoses. The results emphasize the importance of customized preprocessing techniques and the careful selection of suitable deep learning models in improving the field of medical image analysis.
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