article · Scientific Reports
Cervical cancer is a prevalent disease where early detection is critical, but traditional screening methods often rely on subjective interpretation by specialists. This research introduces a novel methodology to enhance cervical cancer detection and classification using a fusion of deep learning and machine learning models. Pre-trained deep neural networks, such as Alexnet, Resnet-101, Resnet-152, and InceptionV3, were fine-tuned for feature extraction from medical images. These features were then processed by various machine learning algorithms. Notably, the ResNet152 model demonstrated exceptional performance, achieving an impressive accuracy rate of 98.08% on the publicly available SIPaKMeD dataset. This innovative hybrid approach aims to improve the accuracy and efficiency of cervical cancer diagnostics through intelligent automation.
Cervical cancer is a significant health issue, and timely, accurate diagnosis is paramount for effective treatment. This research offers a promising pathway to more objective and efficient classification of cervical cancer, potentially reducing diagnostic errors and improving patient outcomes by leveraging advanced artificial intelligence techniques in medical imaging.
This research presents an intelligent automation tool for medical diagnostics, specifically for cervical cancer classification. It could be developed into a decision support system for pathologists and healthcare providers, assisting in the interpretation of screening images. As early-stage research demonstrating high accuracy on a public dataset, it indicates potential for further development towards clinical application as an aid for diagnosis.
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Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.
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DOI: 10.1038/s41598-024-61063-w
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