BREAST CANCER PREDICTING USING DEEP LEARNING WITH ARTIFICAL INTELLIGENCE

Approved

Relevance and Originality

The Research Article addresses a critical issue in women’s health, highlighting breast cancer as a leading cause of death. By focusing on computer-aided diagnosis (CAD) as a means to improve early detection, the study underscores the vital role of artificial intelligence (AI) in the medical field. The originality lies in the exploration of traditional convolutional neural networks (TCNN) and supported convolutional neural networks (SCNN) to enhance diagnostic accuracy, particularly in the context of fuzzy mammography images.

Methodology

The article outlines the application of TCNN and SCNN methodologies for improving cancer detection. However, the methodology section should provide a more comprehensive overview, including details about the dataset, image preprocessing techniques, and specific parameters used in the algorithms. Additionally, clarity on the training and validation processes employed would enhance the robustness of the research framework.

Validity & Reliability

To ensure the validity and reliability of the findings, the Research Article should detail the measures taken to maintain data quality during the collection and analysis of mammography images. Addressing potential biases, such as variations in imaging techniques or patient demographics, is essential. Furthermore, discussing the evaluation metrics used to assess the performance of TCNN and SCNN would strengthen the credibility of the results.

Clarity and Structure

While the content is relevant, the clarity and structure of the Research Article could be improved for better understanding. Organizing the article into distinct sections—introduction, methodology, results, and discussion—would facilitate reader comprehension. Additionally, using clear language and defining technical terms related to CNNs and mammography would make the material more accessible to a wider audience, including those not specialized in medical imaging.

Result Analysis

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