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This study investigates whether traditional, interpretable machine learning (ML) models can serve as a practical alternative to computationally intensive deep learning (DL) systems for classifying skin lesions. The primary objective was to benchmark two 'tree-based ensemble' models—Random Forest and Gradient Boosting—against a lightweight DL model for classifying four types of skin lesions: basal cell carcinoma (BCC), benign keratosis-like lesions (BKL), melanocytic nevi (MN), and melanoma. The core of the research questions the trade-off between the high accuracy of 'black box' DL models and the efficiency and transparency offered by classical ML approaches.
The methodology involved using a public dataset of 8,000 dermoscopic images. Unlike DL models that learn features automatically from raw pixels, this study employed a traditional pipeline based on 'handcrafted' feature engineering. This process involved several steps: standardizing images through preprocessing, then programmatically extracting specific numerical descriptors related to texture (e.g., Local Binary Patterns), color (histograms in the LAB color space), and shape (e.g., Histogram of Oriented Gradients). These features were then used to train an ensemble model, which combines the predictions of both Random Forest and Gradient Boosting classifiers to improve overall performance. The model's accuracy was evaluated on a hold-out test set and compared to a MobileNetV2, a DL model optimized for efficiency.
The study reports that its traditional ML approach achieved high performance, particularly in identifying the most critical lesion types. The Gradient Boosted model reached 89% accuracy, and the final ensemble demonstrated excellent F1-scores (a combined measure of precision and recall) for melanoma (0.92) and melanocytic nevi (0.98). However, the model struggled to distinguish between BCC and BKL, which have similar visual features. A key finding highlighted in the abstract is that the traditional models trained more than 10 times faster than the DL benchmark while achieving comparable performance in melanoma detection. The use of interpretability techniques also confirmed that the model's decisions were based on clinically relevant cues, such as irregular borders and pigmentation.
The paper concludes that for applications like skin lesion classification, traditional ML models present a compelling and viable alternative to deep learning. By offering a balance of strong diagnostic performance, significantly greater computational efficiency, and transparent decision-making, this approach is particularly well-suited for deployment in resource-constrained clinical settings or as point-of-care diagnostic aids where speed and interpretability are paramount.
Overall, the evidence only partially supports the study's conclusion that traditional machine learning models are a robust and practical alternative to deep learning for this task. The paper's primary strength lies in its clear demonstration of high classification performance for clinically critical lesions like melanoma (0.92 F1-score) and its compelling, albeit unsubstantiated, claim of a tenfold efficiency gain. However, the study's reliability is significantly undermined by a major methodological contradiction regarding the image processing pipeline (grayscale vs. color) and a key results figure (Figure 5) that visually contradicts the central premise of using an ensemble model. These unresolved issues weaken confidence in the reported findings.
Major Limitations and Risks: The study's conclusions are constrained by several high-impact issues. First, the methodological contradiction between the described grayscale preprocessing pipeline (Figure 2) and the stated use of color-based features (Methods text) makes the core methodology irreproducible and its results questionable. Second, the graphical evidence in the Results section (Figure 5) showing the ensemble model underperforming its components directly conflicts with the paper's main argument, suggesting a potential flaw in the ensembling process or the data visualization. Finally, the failure to substantiate the crucial '>10x faster' training claim from the abstract with data or discussion in the main text leaves the central comparison to deep learning as an unsupported assertion.
Based on this analysis, the implementation or adoption of this specific model is not recommended. The confidence in its reported performance is Low due to the unresolved methodological contradictions and conflicting evidence within the results. The study serves as a proof-of-concept, but its design limitations prevent it from making reliable claims of superiority or even equivalence to a deep learning benchmark. The most critical next step required to raise confidence would be to resolve the fundamental contradiction in the preprocessing methodology and either correct or explain the counterintuitive results in Figure 5. Following that, a rigorous validation on an external, multi-source clinical dataset would be necessary to assess the model's true generalizability and performance.
The abstract is exceptionally well-organized, adhering to the Introduction, Methods, Results, and Conclusion (IMRaD) structure. Each paragraph is dedicated to one component, allowing readers to quickly grasp the study's purpose, methodology, key findings, and implications without ambiguity. This logical flow significantly enhances readability and comprehension.
The results are presented with specific, quantitative metrics, which lends significant credibility to the findings. Stating exact performance figures like "89% accuracy" and a "macro-averaged F-score of 0.88," along with a direct efficiency comparison of being "more than 10 times faster" than the benchmark, provides a clear and compelling summary of the study's outcomes.
The abstract effectively bridges the gap between technical machine learning research and clinical application by consistently highlighting the practical implications. The emphasis on interpretability, faster training times, and suitability for "resource-constrained settings and point-of-care diagnostic tools" makes the research immediately relevant to a clinical audience.
The abstract effectively contrasts tree-based models with general Convolutional Neural Networks (CNNs). However, the benchmark used is a "lightweight MobileNetV2," a model specifically designed for efficiency. Stating this earlier, perhaps in the introduction, would provide a more nuanced and stronger framing for the study's comparison. It would clarify that the tree-based models are being compared not just to any DL model, but to one already optimized for resource efficiency, making the >10x speed improvement even more impressive. This is a medium-impact suggestion that would enhance the precision of the study's positioning.
Implementation: In the introduction paragraph, modify the sentence discussing CNNs. For instance, change "...particularly convolutional neural networks, have shown promise..." to "...particularly efficient deep learning models like lightweight convolutional neural networks, have shown promise...". This immediately sets the stage for a more direct and fair comparison.
The abstract mentions that SHAP analysis aligned with dermatological heuristics, which is a key strength. However, this claim could be made more concrete. While abstracts must be concise, adding a brief quantitative element or more specific detail about the SHAP findings would elevate the statement from a qualitative observation to a more robust result. For example, specifying that the top-ranked features by SHAP were indeed these clinical cues would add weight. This is a low-impact suggestion aimed at increasing the scientific rigor of the results summary.
Implementation: In the results paragraph, revise the final sentence. Instead of "...highlighted blue-black pigmentation and irregular border texture as the most influential cues...", consider a more specific phrasing like "...identified blue-black pigmentation and irregular border texture as the two most influential predictive features, in agreement with established dermatological heuristics...".
The section masterfully employs the inverted pyramid structure, starting with the broad context of skin diseases as a global health issue, narrowing to the specific challenges of dermatological diagnosis, and culminating in the precise aims of the study. This logical progression effectively guides the reader and establishes the research's relevance and focus.
The paper provides a compelling rationale by clearly identifying the core weaknesses of the current diagnostic standard: it is "time-consuming" and "subject to inter-observer variability." This problem statement directly motivates the need for an automated, objective alternative, strongly justifying the study's purpose.
The final paragraph acts as an effective roadmap, explicitly stating the key stages of the research pipeline. It informs the reader about the use of preprocessing, feature extraction, performance evaluation, and a comparative analysis, which prepares them for the subsequent sections of the paper.
The introduction effectively sets up the problem but misses an opportunity to introduce the paper's central thesis, which is clearly stated in the abstract: the comparison between traditional, interpretable ML models and resource-intensive deep learning (DL) models. Introducing this tension—interpretability and efficiency versus black-box complexity—within the introduction would provide a stronger, more focused narrative hook and better align it with the abstract's compelling framing. This is a medium-impact suggestion that would significantly improve the paper's narrative cohesion from the outset.
Implementation: In the second paragraph, after introducing computer vision, add a sentence that introduces the two main paradigms. For example: "While deep learning approaches like convolutional neural networks have shown high accuracy, their computational cost and lack of interpretability can be barriers to clinical adoption. This study, therefore, explores whether traditional, more transparent machine learning models can provide a viable alternative."
The introduction states the goal is to develop a "robust machine learning (ML) model" but remains generic. Specifying that the study focuses on tree-based ensembles, namely Random Forest and Gradient Boosting, would provide valuable clarity and precision. This detail, already present in the title and abstract, would give the reader a concrete understanding of the specific methods being evaluated from the beginning, rather than waiting for the Methods section. This is a low-impact suggestion for enhancing clarity and specificity.
Implementation: In the final paragraph, modify the first sentence. Change "This study aims to develop a robust machine learning (ML) model..." to "This study aims to develop and benchmark robust tree-based ensemble models, specifically Random Forest and Gradient Boosting, for the classification of four common skin conditions...".
The section is exceptionally well-organized, following a logical and standard machine learning pipeline from data acquisition to model evaluation. This clear, step-by-step structure makes the methodology easy to follow and enhances the study's reproducibility, which is a cornerstone of strong scientific reporting.
The authors consistently provide clear justifications for their selection of specific techniques, linking each choice to a specific goal within the problem domain. For instance, explaining that adaptive thresholding is beneficial for melanoma's irregular edges demonstrates a thoughtful approach that goes beyond simply listing methods, adding to the credibility of the research design.
The section provides a high level of specific detail, including the final image dimensions (128x128), the feature extraction techniques (LBP, GLCM, HOG, LAB), and the exact number of estimators found during hyperparameter tuning (48 for GB, 38 for RF). This specificity is crucial for enabling other researchers to replicate, validate, or build upon the work.
The paper mentions training on a training set and evaluating on a separate test set but omits the specific ratio of this split (e.g., 80% training, 20% testing). This is a fundamental detail in machine learning studies that is essential for reproducibility and for allowing readers to properly contextualize the model's performance metrics. This is a medium-impact suggestion that would significantly improve methodological completeness.
Implementation: In the 'Model training and evaluation' subsection, add a sentence specifying the data split. For example: "The full dataset was partitioned into a training set and an independent test set using an 80/20 split, stratified by lesion class to maintain proportional representation."
The text states that data augmentation was used to mitigate class imbalance, particularly for the underrepresented melanoma class. However, it does not quantify the outcome of this process. Stating the final number of images per class in the augmented training set would provide a clearer picture of how effectively the imbalance was addressed. This is a low-impact suggestion that would add quantitative rigor to the data preparation description.
Implementation: In the 'Data augmentation' section, after mentioning the generation of 10 variants, add a sentence clarifying the result. For instance: "This process resulted in a fully balanced training dataset with [X] images for each of the four lesion categories."
The 'Model selection' subsection mentions that predictions from the RF and GB models are combined using strategies like majority voting or weighted averaging, with the best one chosen via cross-validation. However, the text never specifies which of these strategies was ultimately selected for the final model. This is a minor but important detail for full transparency and reproducibility. This is a low-impact suggestion to enhance methodological precision.
Implementation: At the end of the 'Model selection' paragraph, add a sentence to clarify the final choice. For example: "Based on cross-validation performance, a weighted averaging strategy was selected as the optimal method for combining the classifier predictions."
FIGURE 1: To address class imbalance and enhance generalizability, data augmentation techniques, such as rotation, flipping, scaling, cropping, color jittering, Gaussian blur, and gamma correction, are applied to generate diverse variants of each image, simulating various orientations, spatial settings, and lighting conditions.
FIGURE 2: Preprocessing steps, including grayscale conversion, adaptive thresholding, sharpening, aspect ratio-preserving resizing, and padding, are applied to enhance image uniformity and quality while preserving lesion morphology for effective feature extraction.
FIGURE 3: After preprocessing and augmentation, feature extraction methods, including LBP, LAB color histograms, GLCM, and HOG, are applied to convert images into numerical representations that capture texture, color, and structural characteristics essential for skin lesion classification.
FIGURE 4: Prediction is provided with the percentage of the image classified as basal cell carcinoma, benign keratosis-like lesions, melanocytic nevi, and melanoma.
The section is structured in a clear, logical progression. It begins with the preparatory results of hyperparameter tuning, moves to the high-level summary of model performance via the classification report, and then drills down into a detailed error analysis with the confusion matrix. This top-down approach makes the results easy to follow and interpret.
The authors provide specific, quantitative data for all key results, including per-class precision, recall, and F1-scores, as well as the exact counts of misclassifications between specific classes. This level of detail enhances the scientific rigor of the study, provides a clear picture of the model's behavior, and supports the paper's reproducibility.
The text effectively guides the reader through the data presented in the figures. The narrative does not simply state that a figure exists but actively interprets it, pointing out the most salient features, such as the high F1-scores for melanoma in the classification report and the strong diagonal dominance in the confusion matrix, which enhances comprehension.
The subsection on the Graphical User Interface (GUI) describes a practical application of the model rather than an experimental result of its performance. Its placement within the Results section disrupts the scientific narrative focused on model evaluation metrics. This is a medium-impact suggestion; moving this content would improve the paper's structural adherence to the standard IMRaD format, where results focus on the findings of the study's core hypotheses.
Implementation: Move the entire 'Graphical user interface (GUI) for real-time predictions' subsection, along with its corresponding Figure 4, from the Results section to the end of the 'Materials And Methods' section. This would logically group it with other implementation details.
The paper reports a 'sampled test score of 0.7818' from the tuning phase and a final 'weighted-average F1-score of 0.79'. While the values are consistent, the text misses an opportunity to explicitly connect them. This is a low-impact suggestion to improve narrative cohesion. Adding a sentence that frames the final score as a successful validation of the parameters chosen during tuning would create a stronger logical link between the two evaluation stages.
Implementation: At the end of the 'Hyperparameter tuning and model performance' subsection, add a concluding sentence. For example: 'These results informed the decision to train the final model using these settings for full dataset evaluation, where the configuration was validated by achieving a final F1-score of 0.79.'
FIGURE 5: This study trained and evaluated an ensemble model combining Random Forest and Gradient Boosting classifiers to classify four skin lesion types, using a dataset split for training, testing, and hyperparameter tuning, resulting in strong performance demonstrated by classification reports, confusion matrices, and interpretive analysis.
FIGURE 6: The model shows excellent precision and recall for melanocytic nevi and melanoma, reflected in high F1-scores (0.98 and 0.92), while basal cell carcinoma and benign keratosis-like lesions have moderate performance, resulting in balanced overall macro-average and weighted F1-scores of 0.79.
FIGURE 7: The confusion matrix reveals strong diagonal dominance for melanocytic nevi and melanoma, indicating accurate classification, while notable misclassifications occur between basal cell carcinoma and benign keratosis-like lesions, highlighting feature overlap between these classes.
The discussion effectively moves beyond simply restating the results by providing a clear and plausible explanation for the observed performance differences. Attributing the confusion between basal cell carcinoma and benign keratosis-like lesions to 'overlapping visual characteristics' provides a strong analytical interpretation that grounds the statistical findings in the problem domain.
The section excels at justifying its methodological choices by systematically referencing prior work. For each major component of the pipeline—from feature extraction techniques like LBP to the choice of an ensemble classifier—the authors cite relevant studies, demonstrating that their approach is built upon established and effective practices in the field.
A key strength of the discussion is its forward-looking perspective. It not only identifies current limitations, such as imaging variations and class imbalance, but also proposes specific, state-of-the-art solutions like domain adaptation and GANs. This demonstrates a sophisticated understanding of the field's trajectory and provides concrete directions for future research.
The abstract establishes a compelling narrative by benchmarking the traditional ML models against a DL approach, highlighting a >10x training speed advantage. The discussion section misses a critical opportunity to follow through on this narrative. It focuses on contextualizing its own methods but fails to discuss the implications of its findings relative to the DL benchmark. This is a high-impact suggestion; adding this comparative discussion would directly reinforce the paper's central thesis about the practical advantages of traditional ML in certain contexts.
Implementation: In the first paragraph, after summarizing the model's performance, add a few sentences that explicitly revisit the comparison. For example: 'Crucially, these results were achieved with significantly greater computational efficiency than benchmark deep learning models, as noted in the abstract. This trade-off between the nuanced feature learning of DL and the efficiency of traditional ensembles is a key consideration for deployment in resource-constrained clinical settings.'
The discussion correctly identifies that the main limitation is the confusion between basal cell carcinoma (BCC) and benign keratosis-like lesions (BKL). However, the analysis remains at a technical level ('confusion between visually similar classes'). This is a medium-impact suggestion; expanding on the real-world clinical implications of this specific misclassification pattern would significantly enhance the paper's translational relevance. For example, discussing the consequences of a false negative for BCC or a false positive for BKL would better connect the model's performance to patient outcomes and clinical workflow.
Implementation: At the end of the second paragraph on page 8, add a sentence to elaborate on the clinical context. For example: 'From a clinical perspective, while the confusion is between two less aggressive lesion types compared to melanoma, the potential for misclassifying a carcinoma as benign underscores the need for further refinement before such a tool could be used for anything beyond preliminary screening.'