Machine Learning Algorithms:
Support Vector Machines (SVM)
SVMs are used due to their robustness in high-dimensional spaces and effectiveness in classification tasks. A radial basis function (RBF) kernel is chosen for its capability to handle non-linear relationships[66].
Convolutional Neural Networks (CNN)
CNNs are employed for their proficiency in handling image data and learning spatial hierarchies of features. For the purpose of extracting and classifying features from Contourlet-transformed images, the architecture consists of numerous convolutional layers, pooling layers, and fully linked layers [67].
Training and Validation:
Dataset Split
To make sure the model is properly trained and its performance is appropriately evaluated, the dataset is divided into training, validation, and test sets.
Cross-Validation
In order to prevent over fitting and make sure the model performs effectively when applied to new data, cross-validation techniques like k-fold cross-validation are utilized to validate the model.
Hyper parameter Tuning
The best hyper parameters for SVM and CNN classifiers are found using grid search and random search algorithms [68].
Performance Metrics:
Accuracy
To determine the ideal hyper parameters for SVM and CNN classifiers, grid search and random search techniques are employed [68].
Sensitivity (Recall)
The classifier's accuracy in identifying positive cases, or lesions that are malignant.
Specificity
The classifier's accuracy in identifying negative cases, or benign lesions.
Precision
the percentage of actual positive cases as a percentage of all positive instances.
F1 Score
The harmonic means of recall and precision offer a counterbalance to each other
AUC-ROC (Area Under the Receiver Operating Characteristic Curve)
A comprehensive measure of the classifier’s performance across all classification thresholds [69].
Experimental Results and Comparison
Experiment Setup:
Hardware and Software
Experiments are conducted on a system with high computational power, using software libraries such as Scikit-learn for SVM and TensorFlow or PyTorch for CNN implementation.
Dataset
Training and assessment are conducted using a well-labelled dataset of dermoscopic pictures, such as the ISIC repository. A wide variety of benign and malignant lesions are included in the dataset [70].
Results of Contourlet-Based Approach in Table.1:
Table.1 Results of Contourlet-SVM Performance & Contourlet-CNN Performance
Transforms | Metrics |
Accuracy | Sensitivity | Specificity | Precision | F1 Score | AUC-ROC |
Contourlet-SVM Performance | 91.5% | 89.2% | 93.7% | 90.1% | 89.6% | 0.95 |
Contourlet-CNN Performance | 94.3% | 92.8% | 95.8% | 93.4% | 93.1% | 0.97 |
Comparison with Traditional Methods in Table.2:
Table.2Results ofWavelet Transform-Based Approach&Traditional Image Processing and Feature Extraction
Transforms | Metrics |
Accuracy | Sensitivity | Specificity | Precision | F1 Score | AUC-ROC |
Wavelet Transform-Based Approach | 85.7% | 82.4% | 88.9% | 84.5% | 83.4% | 0.89 |
Traditional Image Processing and Feature Extraction | 80.2% | 78.5% | 82.0% | 79.0% | 78.7% | 0.85 |
Analysis and Discussion:
Enhanced Performance
The Contourlet-based approach significantly outperforms traditional wavelet transform and standard image processing methods in all performance metrics. The superior directional sensitivity and multiscale analysis capabilities of the Contourlet transform contribute to capturing intricate features of skin lesions, leading to higher classification accuracy and robustness[71].
Improved Sensitivity and Specificity
The Contourlet-CNN model demonstrates the highest sensitivity and specificity, indicating its effectiveness in distinguishing between malignant and benign lesions. This makes it particularly valuable for clinical applications where early and accurate detection is critical[72].
AUC-ROC Superiority
The higher AUC-ROC values for Contourlet-based models indicate better overall performance across various thresholds, highlighting the robustness of the approach in practical diagnostic scenarios[72].
Future perspective of Multiresolution Evaluation of Contourlet Transform for the Diagnosis of Skin Cancer
By increasing the precision and effectiveness of image analysis methods, the multiresolution assessment of the contourlet transform presents encouraging developments in the diagnosis of skin cancer.
Enhanced Diagnostic Accuracy
Improved Feature Extraction:
Multiresolution Analysis
Contourlet transform provides a more comprehensive multiresolution representation of skin lesions, capturing intricate texture and edge details that are crucial for distinguishing malignant from benign lesions.
Directional Sensitivity
The ability to analyze images at multiple resolutions and orientations helps in better capturing the structural characteristics of skin lesions, leading to more accurate feature extraction and classification.
Integration with Machine Learning:
Deep Learning Synergy
Combining contourlet transform with deep learning models, such as convolutional neural networks (CNNs), can enhance the model’s ability to learn and generalize from complex image features, improving diagnostic performance.
Hybrid Models
Development of hybrid models that integrate contourlet transform features with other image processing techniques (e.g., wavelet transform) could further boost diagnostic accuracy and robustness.
Efficiency and Automation
Real-time Analysis:
Computational Efficiency
Advances in computational power and optimization algorithms can make the application of contourlet transform in real-time diagnosis feasible, allowing for immediate feedback and early detection during clinical examinations[73–77].
Automated Workflows
Integration into automated diagnostic workflows in dermatology practices can streamline the screening process, reducing the workload on dermatologists and increasing patient throughput.
Portable Diagnostic Tools:
Mobile and Handheld Devices
Implementation of contourlet-based diagnostic algorithms in mobile and handheld devices can make skin cancer screening more accessible, particularly in remote and underserved areas.
Telemedicine Integration
Contourlet transform-based analysis can be incorporated into telemedicine platforms, enabling remote diagnosis and consultation, thus expanding the reach of expert dermatological care[78, 79].
Personalized Medicine and Patient Monitoring
Personalized Risk Assessment:
Longitudinal Analysis
By applying contourlet transform to a series of dermoscopic images over time, it’s possible to track changes in skin lesions, proposing personalized risk assessments and tailored monitoring plans for high-risk patients.
Predictive Analytics
Integrating patient-specific data (e.g., genetic factors, history of sun exposure) with image analysis results can enhance predictive models for skin cancer, helping in the creation of individualized preventative and treatment measures [80–84].
Enhanced Monitoring Systems:
Wearable Technology
Development of wearable devices equipped with high-resolution imaging capabilities and contourlet analysis can facilitate continuous monitoring of skin health, alerting users to potential issues that require medical attention.
AI-assisted Decision Support
Advanced AI systems utilizing contourlet transform can provide dermatologists with decision support, suggesting possible diagnoses and treatment options based on the analyzed images and historical data[85, 86].
Algorithm Refinement
Continuous research on refining the contourlet transform algorithms, addressing challenges such as noise reduction and image enhancement, can lead to even more precise and reliable diagnostic tools.
Clinical Trials
Extensive clinical trials and validation studies are necessary to establish the efficacy and reliability of contourlet-based diagnostic systems, ensuring they meet regulatory standards and are widely accepted in clinical practice.
Cross-disciplinary Collaboration:
Interdisciplinary Research
Collaboration between dermatologists, computer scientists, and engineers is crucial for the ongoing development and implementation of contourlet-based diagnostic systems.
Standardization Efforts
Establishing standardized protocols for image acquisition, processing, and analysis using contourlet transform can facilitate broader adoption and integration into existing diagnostic frameworks.