A Comparative Study of Classification Algorithms for Enhanced Lung Cancer Prediction Using Deep Learning and SOM-Based Microscopic Image Analysis

Approved

Relevance and Originality

This study tackles a critical issue in oncology—early detection of lung cancer—by proposing a hybrid approach that combines deep learning and Self-Organizing Maps (SOM). Given the high mortality rates associated with lung cancer, the relevance of this research is profound, as it aims to improve diagnostic accuracy and timeliness. The originality of the study lies in its integration of advanced deep learning techniques, specifically MobileNet for feature extraction, with traditional machine learning methods for classification. This innovative approach not only contributes to the existing body of knowledge but also addresses the pressing need for more effective diagnostic tools in medical imaging.

Methodology

The methodology outlined in this study is robust, employing a systematic approach to feature extraction and classification. The use of a pre-trained MobileNet model for feature extraction is a strength, as it leverages transfer learning to enhance performance on a specialized task. However, further details on the dataset, including the number of samples, their sources, and any preprocessing steps, would strengthen the methodological rigor. Additionally, a clear explanation of the specific parameters used for the SOM and the selection criteria for the various classifiers would provide deeper insight into the experimental design.

Validity & Reliability

The study demonstrates a good level of validity by utilizing established machine learning techniques and a hybrid model to address lung cancer classification. To enhance reliability, it would be beneficial to detail the validation process for the classifiers used, such as cross-validation techniques or the use of a separate test set. Furthermore, presenting performance metrics such as accuracy, sensitivity, specificity, and ROC curves for each classifier would provide a clearer picture of the model's effectiveness. A discussion of potential biases in the dataset and their implications for the results would also improve the study's reliability.

Clarity and Structure

The paper is generally well-structured, presenting a logical flow from the introduction of the problem to the proposed methodology and results. However, certain sections could benefit from improved clarity. Simplifying complex sentences and avoiding jargon where possible would enhance readability for a broader audience. Additionally, using headings and subheadings to clearly delineate different sections, as well as including visual aids such as diagrams or flowcharts, could further clarify the methodology and results.

Result Analysis

The results section effectively highlights the performance of the hybrid model in classifying lung cancer subtypes, with particular emphasis on the efficacy of Random Forest and Decision Tree classifiers. However, more detailed statistical analysis and comparisons of the classifiers’ performance metrics would enrich this section. Presenting confusion matrices or ROC curves could provide valuable insights into the classification results. Furthermore, discussing the implications of the findings for clinical practice, such as potential applications in diagnostic workflows, would greatly enhance the practical relevance of the research.