BREAST CANCER PREDICTING USING DEEP LEARNING WITH ARTIFICAL INTELLIGENCE

Not Approved

Relevance and Originality

The Research Article addresses a critical issue in women's health, specifically breast cancer, which is the leading cause of cancer-related deaths among women. By focusing on computer-aided diagnosis (CAD) as a tool for early detection, the study underscores the importance of integrating artificial intelligence (AI) in medical practices. The originality of this work lies in its exploration of traditional convolutional neural networks (TCNN) and supported convolutional neural networks (SCNN) to tackle the specific challenges posed by fuzzy mammography images, contributing valuable insights to the field of medical imaging.

Methodology

The article discusses the application of TCNN and SCNN methodologies for improving cancer detection in mammography images. However, it would benefit from a more detailed methodology section that includes specifics about the datasets used (e.g., the number of images, sources), preprocessing steps, and the parameters for the algorithms. Clarifying how the methodologies were implemented and tested would enhance the reader's understanding of the research framework.

Validity & Reliability

To establish the validity and reliability of the findings, the Research Article should elaborate on how data quality was maintained during the collection and analysis of mammography images. Addressing potential biases, such as differences in imaging protocols or patient demographics, is crucial for strengthening the research. Additionally, discussing the evaluation metrics used to measure the performance of TCNN and SCNN—such as accuracy, sensitivity, and specificity—would enhance the credibility of the study's conclusions.

Clarity and Structure

The clarity and structure of the Research Article could be improved for better comprehension. Organizing the content into well-defined sections—such as introduction, methodology, results, and discussion—would facilitate understanding. Using straightforward language and defining technical terms related to CNNs and mammography would make the material more accessible to a broader audience, including those who may not be specialized in medical imaging.

Result Analysis

The analysis of results is essential for demonstrating the effectiveness of the TCNN and SCNN methods in breast cancer detection. The Research Article should present detailed findings that showcase improvements in classification accuracy and comparisons with traditional diagnostic approaches. Incorporating visual representations, such as segmented images or performance graphs, would enhance the presentation of results. A thorough discussion of the implications for clinical practice and any limitations of the study would provide valuable insights into the broader impact of this research on breast cancer diagnosis.