AI Article Synopsis

  • * The model demonstrated high performance metrics including 92% precision, recall, and accuracy, alongside a sensitivity of 89% and specificity of 96%, indicating strong accuracy in identifying malignant cases.
  • * Despite challenges like limited sample size and image quality variations, the research emphasizes the promise of AI in aiding physicians with breast cancer detection, recommending further studies for improved reliability and application in clinical settings.

Article Abstract

This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11058570PMC
http://dx.doi.org/10.7759/cureus.57280DOI Listing

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