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 research article addresses a critical issue in oncology: the need for effective early detection techniques for lung cancer, which is a leading cause of cancer-related deaths globally. The relevance of the study is underscored by its focus on a hybrid approach that combines deep learning with Self-Organizing Maps (SOM), which is innovative in the context of lung cancer subtype classification. The originality of this work lies in its integration of advanced feature extraction techniques using a pre-trained MobileNet model with traditional machine learning algorithms for classification. By exploring this hybrid model, the study contributes valuable insights into improving diagnostic accuracy in lung cancer detection.

Methodology:

The methodology employed in this study is robust, featuring a well-defined hybrid approach that incorporates deep learning for feature extraction and SOM for dimensionality reduction. The use of a pre-trained MobileNet model is particularly advantageous, as it leverages transfer learning to enhance feature extraction efficiency. The subsequent application of various machine learning algorithms, including Random Forest, LightGBM, and Decision Tree, for classification allows for a comprehensive comparison of their performances. However, the paper could benefit from further details regarding the dataset used, including its size, diversity, and any preprocessing steps taken to ensure the quality of the input data. This information would strengthen the methodology's transparency and replicability.

Validity & Reliability:

The study demonstrates strong validity by focusing on well-established methodologies in the field of medical image analysis. The comparative analysis of different classifiers offers a solid basis for evaluating the effectiveness of the proposed hybrid model. To enhance reliability, the inclusion of performance metrics such as accuracy, precision, recall, and F1 scores for each classifier would provide a clearer picture of their predictive capabilities. Additionally, discussing potential limitations of the study, such as any biases in the dataset or challenges faced during model training, would offer a more nuanced perspective on the validity of the results.

Clarity and Structure:

The article is structured logically, guiding the reader through the introduction of the problem, the methodology employed, and the results obtained. However, some sections could be more concise to improve clarity, particularly those that delve into technical details without sufficient explanation. Simplifying complex language and ensuring that technical terms are clearly defined would enhance accessibility for a broader audience. Clearer subheadings to delineate sections on methodology, results, and discussions would further improve the organization of the content, making it easier for readers to navigate.

Result Analysis:

The result analysis effectively highlights the competitive classification accuracy of the hybrid model and its potential in lung cancer detection. By focusing on the performance of Random Forest and Decision Tree classifiers, the study provides valuable insights into the strengths of different machine learning approaches. However, including more detailed comparisons of the classifiers, such as confusion matrices or ROC curves, would enhance the depth of the analysis and allow for a better understanding of their performance nuances. Additionally, discussing the implications of thresholding for highlighting cancerous regions in images could provide further insights into the practical applications of the findings, emphasizing how this research could translate into improved diagnostic tools in clinical settings.