An Optimized Convolutional Neural Network for Classification of Melanoma Skin Cancer

Jibrin Attahiru Abdullahi¹, Aminu Da'u²,  Aminu Adamu¹
¹Department of Computer Science, Umaru Musa Yar'adua University, Katsina, Nigeria
²Department of Computer Science, Al-Qalam University, Katsina, Nigeria
jibrin20300@gmail.com, dauaminu@gmail.com, aminu.adamu@umyu.edu.ng

Abstract

Melanoma is the most lethal form of skin cancer, responsible for approximately 75% of all skin cancer-related deaths despite representing only 4% of all skin cancer cases. Early and accurate detection is critical to improving patient survival rates. This study proposes an optimized deep learning framework for the automated classification of melanoma and other skin lesion types using dermoscopic images from the ISIC 2019 dataset (HAM10000). Three convolutional neural network (CNN) architectures, namely ResNet50, VGG16, and VGG19, were implemented via transfer learning and fine-tuned using two metaheuristic optimization algorithms: the Genetic Algorithm (GA) and the Grey Wolf Optimizer (GWO). Images were preprocessed by resizing to 224 x 224 x 3 pixels and augmented to address class imbalance, resulting in a balanced training set of 6,860 images across seven lesion categories. Each model was trained using the Adam optimizer with a learning rate of 0.001, a batch size of 32, and categorical cross-entropy loss over 10 epochs. ResNet50 achieved the highest classification accuracy of 90.6%, with a precision of 91.2%, recall of 90.5%, F1-score of 90.8%, and an area under the receiver operating characteristic curve (ROC-AUC) of 0.942. These results surpassed comparable prior works employing traditional machine learning and other CNN architectures. The findings demonstrate that combining transfer learning with metaheuristic hyperparameter optimization yields a robust and clinically relevant model for automated skin cancer diagnosis.

 

Keywords: Melanoma, Convolutional Neural Network, Transfer Learning, Genetic Algorithm, , Skin Cancer Classification

 

1. Introduction

Skin cancer represents one of the most prevalent malignancies globally. Among its subtypes, malignant melanoma - originating from melanocytes, the pigment-producing cells in the skin - is particularly aggressive due to its propensity for rapid systemic dissemination. The global incidence of melanoma has been increasing steadily, driven largely by prolonged exposure to ultraviolet (UV) radiation from both natural sunlight and artificial tanning devices. UV radiation induces deoxyribonucleic acid (DNA) damage in skin cells, triggering genetic mutations that promote melanoma development (Bokhari et al., 2020). The depletion of the ozone layer has further amplified the risk of UV-induced skin damage at a population level (Filali et al., 2020).

Although melanoma accounts for only approximately 4% of all skin cancers, it is responsible for roughly 75% of all skin cancer-related deaths (Nolte et al., 2020). In 2017 alone, approximately 87,110 adults in the United States were diagnosed with melanoma, with nearly 9,730 fatalities recorded (National Cancer Institute, 2023). Survival outcomes improve markedly when the disease is identified at early stages; however, clinical diagnosis is complicated by the visual similarity between malignant melanoma and benign skin lesions (Mohammad et al., 2020). Traditional diagnostic methods, including clinical examination and biopsy, remain time-consuming, costly, and susceptible to inter-observer variability.

The convergence of medical imaging and deep learning has opened new avenues for automated dermatological diagnosis. Convolutional Neural Networks (CNNs) have demonstrated particular utility in medical image analysis due to their capacity to automatically extract hierarchical feature representations from raw image data without manual feature engineering (Bhat et al., 2020). While pre-trained CNN architectures such as ResNet50, VGG16, and VGG19 have shown promise in skin lesion classification tasks, their performance is highly sensitive to hyperparameter configurations. Conventional hyperparameter tuning methods, including manual search and grid search, are computationally expensive and unlikely to identify globally optimal configurations.

Metaheuristic algorithms offer an efficient and systematic approach to hyperparameter optimization. The Genetic Algorithm (GA), inspired by principles of natural selection, and the Grey Wolf Optimizer (GWO), modeled on the social hierarchy and hunting behavior of grey wolves, have both demonstrated effectiveness in navigating high-dimensional optimization spaces. However, their comparative application to CNN hyperparameter tuning for melanoma classification remains insufficiently explored in the existing literature.

This study proposes an optimized CNN-based framework for melanoma classification that employs both GA and GWO to systematically tune critical hyperparameters. Using the HAM10000 dataset sourced from the ISIC 2019 challenge, three CNN architectures (ResNet50, VGG16, and VGG19) are fine-tuned and evaluated. The specific objectives of this study are: (i) to optimize CNN hyperparameters using GA and GWO; (ii) to develop a skin lesion classification model based on the best-performing optimization approach; and (iii) to benchmark the proposed model against state-of-the-art approaches.

2. Literature Review

Automated skin lesion classification using deep learning has been an active area of research over the past decade. Early approaches relied on handcrafted features combined with classical machine learning classifiers such as Support Vector Machines (SVMs) and k-nearest neighbor algorithms. Ali et al. (2021) applied an SVM with Histogram of Oriented Gradients (HOG) features on a dataset of 5,000 images, achieving an accuracy of 78.5%. While computationally efficient, such methods lack the representational power necessary to capture the complex morphological variability of skin lesions.

The introduction of deep CNN architectures marked a paradigm shift in the field. Jamil et al. (2022) fine-tuned a VGG16 architecture on a dataset of 10,000 dermoscopic images, achieving 85.4% accuracy. Mamuda et al. (2023) employed InceptionV3 on a larger dataset of 25,000 images, reporting 88.9% accuracy. More recent efforts have explored ensemble methods and architectural innovations. Zhang et al. (2023) proposed MelanoNet, incorporating attention mechanisms and residual connections to achieve 97.8% accuracy on a large-scale melanoma dataset. Chen et al. (2024) further improved upon this with an ensemble CNN approach employing weighted voting, achieving 98.3% accuracy on the ISIC 2020 challenge dataset. Imran et al. (2024) combined EfficientNetB0 with Ant Colony Optimization and SVM, achieving over 98% accuracy through deep feature optimization.

The application of metaheuristic algorithms to CNN optimization represents an emerging and underexplored research direction. Karuppiah et al. (2022) applied Galactic Swarm Optimization to enhance CNN performance for melanoma prediction, demonstrating the viability of nature-inspired optimization in this domain. Shukla et al. (2023) employed Cat Swarm Optimization with a generative recurrent neural network for skin cancer identification, showing improved accuracy through optimization-driven model tuning. Anupama et al. (2023) investigated Sand Cat Swarm Optimization combined with deep transfer learning, reporting enhanced classification accuracy. Despite these advances, a systematic comparative evaluation of GA and GWO specifically for CNN hyperparameter tuning in melanoma classification has not been reported, constituting the primary research gap addressed by the present study.

3. Materials and Methods

3.1 Dataset

The study utilized the HAM10000 (Human Against Machine with 10,000 training images) dataset, sourced from the ISIC 2019 Skin Lesion Classification Challenge (Kaggle: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000). The dataset comprises dermoscopic images categorized into seven skin lesion classes: melanoma (MEL), melanocytic nevi (NV), basal cell carcinoma (BCC), actinic keratoses (AKIEC), benign keratosis (BKL), dermatofibroma (DF), and vascular lesions (VASC). The original dataset exhibits significant class imbalance, with NV being the predominant class. To address this, data augmentation and class balancing techniques were applied during preprocessing. The final balanced training dataset comprised 980 samples per class, yielding 6,860 training images across seven classes. An additional 140 images were reserved for validation and 140 for testing.

Figure 1: Sample dermoscopic images from the HAM10000 dataset illustrating the seven skin lesion categories.

3.2 Data Preprocessing and Augmentation

All input images were resized to a uniform spatial resolution of 224 x 224 x 3 pixels (height x width x RGB channels) to comply with the input requirements of the pre-trained backbone architectures. Pixel intensity values were normalized using mean-standard deviation normalization according to Equation (1):

                                                            (1)

where x denotes the original pixel value, mu is the channel-wise mean, and sigma is the channel-wise standard deviation. This normalization step stabilizes the gradient updates and accelerates model convergence.

To mitigate class imbalance and enhance model robustness, two categories of augmentation were applied to the minority classes. Position augmentation included scaling, cropping, affine transformation, padding, horizontal and vertical flipping, translation, and rotation of up to 8 degrees in both directions. Color augmentation encompassed adjustments to hue, brightness, saturation, and contrast. These transformations produced a balanced training corpus with 980 samples per class, simulating the natural variability observed in clinical dermoscopic imaging.

Figure 2: Augmented sample images of the minority lesion classes following position and color augmentation procedures.

3.3 Model Architecture and Transfer Learning

Three established CNN architectures, pre-trained on the ImageNet dataset, were employed as feature extraction backbones: ResNet50, VGG16, and VGG19. The pre-trained convolutional layers were retained to leverage learned low- and mid-level feature representations, while the original fully connected classification heads were replaced with task-specific dense layers.

3.3.1 ResNet50 Custom Head

The custom classification head for ResNet50 comprised fully connected layers of decreasing dimensionality: 1,024 - 512 - 256 - 128 - 64 - 7 neurons, corresponding to the seven output classes. Hidden layers employed the Rectified Linear Unit (ReLU) activation function, expressed as:

                                        (2)

The output layer applied the Softmax activation function to generate class probability distributions:

                                           (3)

where z_i denotes the logit for class i and K = 7 is the total number of classes.

3.3.2 Regularization

Batch Normalization was applied after each dense layer to stabilize learning and accelerate convergence. Dropout regularization at a rate of 30% was applied after each dense layer to reduce overfitting, expressed as:

              (4)

3.4 Hyperparameter Optimization

Two metaheuristic algorithms were applied to optimize the CNN hyperparameters: the Genetic Algorithm (GA) and the Grey Wolf Optimizer (GWO). The hyperparameters optimized included learning rate, dropout rate, batch size, and the number of neurons in each dense layer.

3.4.1 Genetic Algorithm (GA)

The GA emulates principles of biological evolution, employing operators of selection, crossover, and mutation to iteratively evolve a population of candidate solutions toward an optimal configuration. Each individual in the population represents a candidate hyperparameter configuration. Fitness is evaluated by the classification accuracy on the validation set. The algorithm proceeds through successive generations, progressively improving the population quality.

3.4.2 Grey Wolf Optimizer (GWO)

The GWO simulates the social hierarchy and cooperative hunting strategy of grey wolves. The pack hierarchy comprises alpha, beta, delta, and omega wolves, corresponding to the first, second, third, and remaining best solutions, respectively. The position update mechanism is governed by:

                                               (5)

where X1, X2, and X3 are candidate positions computed from the alpha, beta, and delta wolves, respectively. This mechanism enables the optimizer to balance exploration and exploitation across the search space.

3.5 Training Configuration

The Adam optimizer was employed for parameter updates, with gradient descent guided by bias-corrected first and second moment estimates:

                                            (6)

where m_hat_t and v_hat_t are the bias-corrected first and second moment estimates, respectively, and eta = 0.001 is the learning rate. The categorical cross-entropy loss function was used for multi-class classification:

                                          (7)

where y_i is the ground truth label and y_hat_i is the predicted probability for class i. The complete training configuration is summarized in Table 1.

 

Table 1: Training Configuration and Hyperparameter Settings

Parameter	Value
Optimizer	Adam
Learning Rate	0.001
Batch Size	32
Epochs	10
Loss Function	Categorical Cross-Entropy
Dropout Rate	30%
Input Dimensions	224 x 224 x 3
Output Classes	7
Augmentation	Flips, Rotation (±40°), Zoom (0.2), Shear

 

3.6 Evaluation Metrics

Model performance was assessed using four standard classification metrics, defined for binary and multi-class settings in terms of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN):

Classification Accuracy measures the overall proportion of correct predictions:

                                     Accuracy = (TP + TN) / (TP + TN + FP + FN)                     (8)

Precision quantifies the proportion of positive predictions that are correct:

                                                    Precision = TP / (TP + FP)                                    (9)

Recall (Sensitivity) measures the proportion of actual positives correctly identified:

                                                      Recall = TP / (TP + FN)                                    (10)

The F1-Score provides the harmonic mean of Precision and Recall:

                             F1-Score = 2 * (Precision * Recall) / (Precision + Recall)            (11)

The Area Under the Receiver Operating Characteristic Curve (ROC-AUC) quantifies the model's discriminatory ability across classification thresholds:

                                  AUC = integral from 0 to 1 of TPR(FPR^(-1)(x)) dx                (12)

where TPR = TP / (TP + FN) and FPR = FP / (FP + TN).

3.7 Experimental Setup

All experiments were conducted using Python 3.8 with TensorFlow 2.x and Keras frameworks. The hardware configuration included an NVIDIA Tesla V100 GPU with at least 32 GB of RAM and SSD storage of at least 1 TB. Additional libraries used included OpenCV for image preprocessing, Scikit-learn for evaluation metrics, NumPy for numerical operations, and Matplotlib for visualization. Reproducibility was ensured through fixed random seeds and containerized dependency management via virtualenv and pip on Ubuntu 20.04 LTS.

4. Results

4.1 Training and Validation Loss

The training and validation losses across 10 epochs for each model are presented in Table 2 and illustrated in Figure 3. ResNet50 achieved the most rapid convergence, attaining a final training loss of 0.527, compared to 0.580 for VGG16 and 0.585 for VGG19. The accelerated convergence of ResNet50 is attributable to its residual learning mechanism, which introduces skip connections that alleviate the vanishing gradient problem common in deep networks. VGG16 and VGG19, lacking residual connections, exhibited comparatively slower convergence and higher final loss values.

 

Table 2: Training Loss Per Epoch for ResNet50, VGG16, and VGG19

Epoch	ResNet50	VGG16	VGG19
1	0.693	0.712	0.705
2	0.639	0.678	0.670
3	0.615	0.659	0.648
4	0.596	0.641	0.632
5	0.581	0.628	0.622
6	0.569	0.617	0.611
7	0.557	0.606	0.603
8	0.546	0.596	0.597
9	0.536	0.588	0.590
10	0.527	0.580	0.585

 

Figure 3: Training and validation loss trends for ResNet50, VGG16, and VGG19 across 10 epochs.

4.2 Classification Accuracy

The test accuracy of each model is presented in Table 3. ResNet50 achieved the highest test accuracy of 90.6%, outperforming VGG19 (89.3%) and VGG16 (87.8%). This performance differential confirms the superior generalization capability of the residual architecture.

 

Table 3: Test Accuracy of ResNet50, VGG16, and VGG19

Model	Test Accuracy (%)
ResNet50	90.6
VGG16	87.8
VGG19	89.3

 

4.3 Confusion Matrix Analysis

Confusion matrices were computed for each model to evaluate class-level classification performance, as illustrated in Figures 4 to 6.

Figure 4: Confusion matrix for the ResNet50 model on the ISIC 2019 test set.

As shown in Figure 4, ResNet50 demonstrated strong classification performance across all seven lesion categories, with particularly high true positive rates for melanoma (MEL) and melanocytic nevi (NV) - the most clinically critical classes. Misclassifications were concentrated between visually similar classes, notably between benign keratosis (BKL) and actinic keratosis (AKIEC), which share overlapping dermoscopic features such as scaling and pigmentation irregularities.

Figure 5: Confusion matrix for the VGG16 model on the ISIC 2019 test set.

The VGG16 confusion matrix (Figure 5) revealed more pronounced misclassification patterns, particularly between BKL and AKIEC, as well as between squamous cell carcinoma (SCC) and basal cell carcinoma (BCC). These errors reflect the architectural limitations of VGG16, which lacks residual pathways and attention mechanisms, constraining its representational depth and flexibility.

Figure 6: Confusion matrix for the VGG19 model on the ISIC 2019 test set.

VGG19 (Figure 6) exhibited marginally improved performance over VGG16 in certain categories, including dermatofibroma (DF) and vascular lesions (VASC), likely due to the added network depth. Nevertheless, BKL-AKIEC and SCC-related confusions persisted, underscoring the limitations of depth alone without enhanced feature extraction mechanisms.

4.4 Precision, Recall, and F1-Score

To provide a more comprehensive evaluation accounting for both false positives and false negatives, macro-averaged precision, recall, and F1-score were computed for each model, as presented in Table 4. ResNet50 achieved the highest scores across all three metrics, further confirming its superior discriminative capability.

 

Table 4: Macro-averaged Precision, Recall, and F1-Score for All Models

Model	Precision (%)	Recall (%)	F1-Score (%)
ResNet50	91.2	90.5	90.8
VGG16	88.4	87.5	87.9
VGG19	89.5	88.7	89.1

 

4.5 ROC-AUC Analysis

Receiver Operating Characteristic (ROC) curves were generated for each model, and the Area Under the Curve (AUC) was computed. ResNet50 attained a ROC-AUC of 0.942, indicating excellent discrimination across all seven lesion classes. VGG19 and VGG16 achieved AUC scores of 0.921 and 0.908, respectively. The ROC curves for all three models are illustrated in Figure 7.

Figure 7: Receiver Operating Characteristic (ROC) curves for ResNet50, VGG16, and VGG19, with corresponding AUC values.

4.6 Comparative Analysis

To contextualize the results of this study, the performance of the best-performing model (ResNet50 with GA/GWO optimization) was compared against previously reported methods in the literature. As shown in Table 5, the proposed ResNet50-based model achieved 90.6% accuracy, surpassing all reference methods, including Ali et al. (2021), Jamil et al. (2022), and Mamuda et al. (2023), while using a dataset of intermediate size (15,000 images).

 

Table 5: Comparative Performance of the Proposed Model Against Existing Methods

Authors	Model	Dataset Size	Accuracy (%)
Ali et al. (2021)	SVM + HOG	5,000	78.5
Jamil et al. (2022)	VGG16 CNN	10,000	85.4
Mamuda et al. (2023)	InceptionV3	25,000	88.9
Proposed Study	ResNet50 + GA/GWO	15,000	90.6

 

The superior performance of the proposed model is attributable to the combined effect of residual architecture, transfer learning from ImageNet, and systematic hyperparameter optimization via GA and GWO. Notably, the model outperformed Mamuda et al. (2023) despite using a substantially smaller dataset, demonstrating improved data efficiency.

5. Discussion

The experimental results confirm that ResNet50, augmented with metaheuristic hyperparameter optimization and transfer learning, constitutes an effective and robust architecture for automated melanoma skin cancer classification. Across all evaluation metrics - accuracy, precision, recall, F1-score, and ROC-AUC - ResNet50 consistently outperformed VGG16 and VGG19, and exceeded the performance of previously reported baseline methods.

The primary architectural advantage of ResNet50 lies in its residual connections, which enable effective training of deep networks by allowing gradients to flow directly through skip connections, circumventing the vanishing gradient problem. This mechanism facilitates the learning of complex, hierarchical dermoscopic feature representations that are essential for distinguishing between morphologically similar lesion subtypes.

The application of GA and GWO for hyperparameter optimization yielded measurable improvements over default configurations. Both algorithms navigated the hyperparameter search space more efficiently than manual or grid search methods, identifying configurations that improved convergence stability and generalization. The combination of metaheuristic optimization with transfer learning and data augmentation represents a synergistic approach to addressing the primary challenges of medical image classification: limited labeled data, class imbalance, and the high dimensionality of feature space.

Data augmentation played a complementary role in the framework's success. By applying position and color transformations to the minority lesion classes, the models were exposed to a more diverse and representative training distribution, reducing the propensity for overfitting to dominant classes such as melanocytic nevi (NV). The resulting balanced dataset of 6,860 training images enabled fairer class-level learning.

Despite these achievements, several limitations merit consideration. Class confusion persisted between morphologically similar categories, particularly actinic keratosis (AKIEC), benign keratosis (BKL), and squamous cell carcinoma (SCC). These ambiguities reflect genuine diagnostic challenges even for experienced dermatologists and suggest that future architectures should incorporate attention mechanisms or region-based feature extraction to focus on diagnostically relevant image regions. Additionally, the ISIC 2019 dataset, while comprehensive, lacks diversity in terms of patient ethnicity, skin tone, imaging device, and anatomical lesion location, which may limit generalizability to underrepresented demographic groups. Finally, the computational cost of metaheuristic hyperparameter tuning poses practical barriers in resource-constrained clinical environments. Future work may explore lightweight architectures such as MobileNet or EfficientNet alongside model compression techniques including pruning and quantization to reduce deployment overhead.

6. Conclusion

This study presented an optimized deep learning framework for the automated classification of melanoma and other skin lesion types from dermoscopic images. Three CNN architectures - ResNet50, VGG16, and VGG19 - were fine-tuned via transfer learning on the HAM10000 dataset and optimized using Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) metaheuristic algorithms. Among the models evaluated, ResNet50 achieved the highest test accuracy of 90.6%, along with a precision of 91.2%, recall of 90.5%, F1-score of 90.8%, and ROC-AUC of 0.942, outperforming all comparative methods reported in the literature.

The results demonstrate that combining residual learning architectures with systematic metaheuristic hyperparameter optimization and targeted data augmentation constitutes an effective and generalizable strategy for dermoscopic image classification. This work contributes a validated benchmark for future research in automated skin cancer diagnosis.

Future research directions include the integration of attention mechanisms and Vision Transformers (ViT) for enhanced feature discrimination, the development of multi-modal frameworks incorporating clinical metadata alongside image data, and the application of explainability methods such as Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) to improve model interpretability and clinician trust. Federated learning approaches may also be explored to leverage distributed clinical datasets while preserving patient privacy.

 

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