Modified Adaptive CNN for Deep Learning based Histopathological Image Analysis for Cancer Diagnosis

Abstract

This research on cancer diagnosis is motivated by the need for more accurate and efficient diagnostic tools in the field of oncology. Traditional methods of histopathological examination are time-consuming and can be subject to human error. The adoption of deep learning techniques, particularly Convolutional Neural Networks (CNNs), presents an opportunity to significantly improve the precision and speed of cancer diagnosis through automated analysis of tissue samples. This research addresses current challenges, including variability in samples and limited dataset diversity, with the goal of enhancing the reliability and applicability of the developed models. The ultimate aim is to contribute to the advancement of diagnostic methodologies, providing clinicians with more reliable tools for early and accurate cancer detection and ultimately improving patient outcomes and treatment strategies. Using a modified adaptive CNN algorithm, trained on extensive pathology image datasets, the approach enhances the precision and speed of cancer cell detection in tissue samples. By utilizing these artificial intelligence tools, the diagnostic process becomes not only more accurate but also significantly expeditious. Despite the promising strides, certain challenges persist. Variability in tissue samples, limited availability of diverse datasets, and interpretability of deep learning models pose hurdles to widespread adoption. To address these challenges, this research focuses on developing robust models that can generalize well across diverse datasets. Additionally, efforts are being made to create more comprehensive and diverse datasets to improve model training. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 95%, F1 score reaching 96%, and precision at 96%.