Abstract
Cervical cancer is becoming one of the most frequent malignancies among women. It is also lethal in its last stages. Squamous cell carcinoma is the most common and dangerous kind of cervical cancer, and it must be discovered early to avoid catastrophic complications. Liquid-based cytology (LBC) swabs, which are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis, are the most successful and extensively used swabs for cervical cancer screening. Using microscopes for manual diagnosis has its limitations and is prone to human mistake. It is also challenging to follow every cell. Therefore, the development of computational approaches is crucial since it allows for the automatic, rapid, and efficient diagnosis of a large number of samples, which is advantageous for medical laboratories and healthcare experts. In order to extract features from filtered images by applying a wiener filter, the suggested system uses a stacked ensemble classifier model, which combines two neural network models such as long short-term memory (LSTM) with gated recurrent units (GRU). The stochastic gradient descent is an optimizer or algorithm which is used to adjust the attributes like weights and learning rate to get better accuracy. Additionally, gray-level co-occurrence matrix (GLCM), a texture analysis tool in digital image processing, is used. With the GLCM feature extractor, the suggested model yields 99% accuracy, 97.6% precision, 94.3% recall, and 91.23% F1 score. The hybrid model's performance is compared to that of the LSTM and GRU classifiers in the research.
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Data Availability
The corresponding author can provide the datasets upon request.
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The research work was made possible with the support of Theivanai Ammal College of Women, Thiruvalluvar University, Tamil Nadu, India which provided the necessary facilities.
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Shanthi, K., Manimekalai, S. Cervical Cancer Detection Using Ensemble Neural Network Algorithm with Stochastic Gradient Descent (SGD) Optimizer. SN COMPUT. SCI. 5, 1151 (2024). https://doi.org/10.1007/s42979-024-03365-4
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DOI: https://doi.org/10.1007/s42979-024-03365-4