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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025

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Skin Cancer Classification with CGAN-Based Data Augmentation

Balaji. K.,

Priya. R

MCA Student, Department of Computer Application-PG VISTAS

Professor, Department of Computer Application-PG VISTAS

DOI : https://doi.org/10.51583/IJLTEMAS.2025.140400057

Received: 01 May 2025; Accepted: 06 May 2025; Published: 13 May 2025

Abstract: Detection of skin cancer remains a crucial medical issue because it determines the effectiveness of melanoma

treatment. The current detection systems experience performance limitations because of limited labeled data which results in

overfitted models that produce narrow potential outcomes while demonstrating poor generalization for unknown skin lesion

classes. This research proposes solving classification challenges through the implementation of Conditional Generative

Adversarial Networks which produces synthetic images that replicate the natural variability seen in real-world skin lesion scans.

Synthetic images from CGANs enhance CNN training sets while improving their capabilities to identify various types of skin

cancer. The proposed system exists as a platform which trains CGANs on real skin lesion datasets to produce matching synthetic

imagery that amalgamates with original datasets before creating an extended CNN training set. The evaluation of proposed CNN

models uses real skin lesion images with their performance evaluated through accuracy and sensitivity while measuring specificity

and F1 score metrics. Model performance improves when training augmentation techniques are used instead of original image sets

resulting in enhanced robustness and precision together with generalized results. Additional incorporation of synthesized data

leads to substantial advancement in detecting skin conditions which dermatologists identify rarely. The research has established

CGANs as promising tools for generating synthetic medical images which address data deficit challenges while showing

foundationally that data augmentation strengthens deep learning model capabilities.

Keywords: Skin cancer detection, Conditional Generative Adversarial Networks (CGANs), Convolutional Neural Networks

(CNNs), Synthetic data augmentation, Rare lesion detection, Medical image classification, Deep learning, Data scarcity.

I. Introduction

Thousands of new skin cancer cases worldwide occur each year making it one of the most frequently reported cancer types [1].

The proper identification of skin lesions along with preliminary categorization remains essential to improve patient results and

minimize treatment expenses according to research [2]. Deep learning and its specific subsection of machine learning currently

experience significant momentum since they represent groundbreaking potential for skin cancer detection as reported in [3]. The

most effective solution for complex medical image processing comes from Convolutional Neural Networks (CNNs) [4].

Large-scale quality datasets present a challenge despite their dependence for these models because medical imaging field

encounters institutional restrictions alongside data privacy problems and expensive annotation requirements [5]. Accurate training

of deep learning models for skin cancer detection faces strong resistance due to minimal available labeled data. The use of small

datasets with insufficient samples leads to overfitting most notably when conflicting with rare lesion types as reported in literature

[6]. The standard augmentation methods which include rotation and scaling and flipping prove insufficient because they fail to

produce genuinely new data [7]. The situation calls for innovative solutions to handle the critical problem of data scarcity in

medical fields.

The challenge of data scarcity finds an apt solution through Generative Adversarial Networks (GANs). The deep learning

technique GANs develops knowledge about data probability distributions while producing falsified samples which mimic

authentic examples [8]. GANs enable researchers to produce extended collections of skin lesion photographs that exhibit

authentic diversity and complexity related to natural clinical scenarios. Synthetic images created by deep learning produce better

training outcomes when combined with real images to boost dataset size for superior detection of miscellaneous skin cancer types

[9]. Our research introduces a model combination between GANs alongside CNNs to address current weaknesses experienced in

skin cancer detection systems. GANs process real datasets to produce synthetic skin images which greatly expand training dataset

capabilities. The trained CNN models assess the effects of synthetic data enhancement on real image performances through

evaluations of accuracy and robustness along with generalization measurements.

This research investigates the issue of finding rare and less frequent lesion types which many current systems fail to recognize

properly. Healthy examination of rare lesions remains essential because misdiagnosing them can produce poorer treatment results

for patients [10]. The proposed system produces simulated lesion types as synthetic examples to boost detection rates throughout

a broader diagnostic framework.

The research has several implications. This research proposes GAN-based methodology as an answer for solving the medical

imaging data shortage. Synthetic data augmentation techniques enhance the functionality of CNN models specifically for

diagnosing skin cancer according to the research. Through GAN technology researchers can explore solutions for detecting weak

lesions while accelerating the advancement of advanced diagnostic frameworks. Research analyzes model performance metrics

and unveils key benefits and drawbacks of using artificial data for deep learning medical image sorting methods.

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The presented work bridges the operational divide that exists between large medical imaging datasets and current data

accessibility barriers. The proposed skin cancer detection system leverages GANs in combination with CNNs which has

established itself as the new standard for this application. Beyond advancements in medical image analysis this research brings

critical findings regarding the potential of synthetic data across Artificial Intelligence applications and healthcare systems.

II. Literature Survey

Research into skin cancer screening methods has proven highly active due to skin cancer's deadly outcomes along with the

increasing worldwide incidence rates. Tests conducted during the last few years prove how machine learning with deep learning

approaches specifically helps automatic skin lesion analytical processes. In this study authors assess exceptional work in skin

cancer detection using CNNs and associated approaches while examining breakthroughs and obstacles throughout the newly

developed domain.

Table 1: Summary of Literature on Skin Cancer Detection Using CNNs

Author(s)

Focus

Key Findings

Limitations

Hasan et al.

Binary classification of

skin cancer lesions using

CNNs

Achieved good accuracy on publicly

available datasets.

Limited to binary

classification (melanoma vs. non-

melanoma).

Zhang et al.

Fine-tuning CNNs for

melanoma and non-

melanoma classification

Improved accuracy and reduced

computational time.

Limited to specific lesion types.

Brinker et

al.

Systematic review of

CNNs in skin cancer

detection

Demonstrated that CNNs can achieve

expert-level accuracy in skin cancer

diagnosis.

Did not address data scarcity or

rare lesion detection.

Shah et al.

ANN and CNN with data

augmentation

Improved detection rates for rare

lesion types using synthetic data

augmentation.

Did not explore advanced GAN

architectures for synthetic data

generation.

Fu’adah et

al.

CNN with synthetic

variations for IS

classification

Enhanced performance across

variable skin lesion classes using

synthetic data.

Focused on specific datasets,

lacking generalization.

Han et al.

Region-based CNN (R-

CNN) for keratinocytic

skin cancer detection

Region-based approaches were

shown to be suitable for medical

images.

Focused only on facial lesions.

Nahata and

Singh

Combination of CNNs

with other deep learning

methods

Improved diagnostic performance and

reliability through hybrid approaches.

High computational cost and

complexity.

Rezaoana et

al.

Parallel CNN and

ensemble learning for skin

cancer classification

Enhanced efficiency and accuracy

through ensemble learning.

Limited exploration of rare lesion

types.

Saba et al.

Heterogeneous framework

for CNN feature fusion

Improved classification outcomes

using feature fusion methods.

Computationally intensive; may

not be suitable for real-time

applications.

Tschandl et

al.

Integration of CNNs with

expert iagnosis

Improved diagnosis of non-

pigmented skin cancers by

combining CNNs with expert inputs.

Focused primarily on non-

pigmented cancers.

Haggenmül

ler et al.

Systematic review on AI

in dermatology

Highlighted the importance of AI in

dermal decision-making processes.

Limited focus ontechnical

advancements in CNNs.

Garg et al.

Real-time diagnosis using

CNNs

Proposed a CNN-based diagnostic

system integrated with smart clinical

workflows.

Did not explore GAN-based data

augmentation techniques.

Malo et al.

Explainable AI models for

skin cancer detection

Emphasized the importance of

explainability in AI models for

clinician trust.

Limited implementation of

explainable AI in skin cancer

detection systems.

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Thurnhofer-

Hemsi and

Domínguez

Lightweight CNN models

for rural healthcare

Suggested lightweight models for

low-resource environments.

Did not address the challenge of

data scarcity.

Dorj et al.

CNN-based skin cancer

classification

Demonstrated CNNs’ capability to

classify common skin cancers.

Did not address rare lesion

classification.

Table 1 Literature review

III. Proposed Methodology

A proposed CNN methodology uses GANs to boost skin lesion differentiation sensitivity and specificity when handling limited

labeled data. The framework includes four core steps described in the following subsections which focus on data preprocessing

and synthetic data generation and CNN training and assessment capabilities.

Preprocessing and Dataset Preparation

The ameliorative research methodology behind this work begins with obtaining a high-quality database containing both skin lesion

images of benign and malignant conditions. Gan is conducting preprocessing of images to improve consistency and preparation for

model training. A standard normalization process sizes all images until they fulfill the necessary requirements both for GAN and

CNN processing. The scale normalization of pixel values protects against value overflow that might happen during learning.

Additional image processing techniques including frame flipping and rotation with scaling help boost the dataset while reducing

low-quality output overfitting. Forward Data Preparation steps transform data into its optimal state prior to successive pipeline

enhancements.

Synthetic Data using GANs

One key aspect of this methodology allows it to resolve issues pertaining to insufficient labeled data availability. The GAN model

generates lifelike imitations of skin lesion images to achieve this objective. Within the GAN model structure two essential

components function as the generator network together with the discriminator network. The generator network trains itself to

generate synthetic skin lesions whose distributions reflect those of authentic samples while the discriminator network develops

skills to separate the genuine from synthetic samples. The generator network receives iterative training through adversarial

procedures to create images near authentic lesions. The combination of original set images gets supplemented with newly crafted

visuals which forms fundamental infrastructure for data expansion toward specific requirements.

Ensembling of Overlapping Data from Synthetic Images

Training data is enhanced through the addition of synthetic images generated by the GAN system along with original training

content. The additional data enhances both experimental variety and sample amount which increases the CNN model's

performance on unpredicted data inputs. The augmented data resolved data scarcity for rare lesions thus improving the

operational strength of the overall system.

CNN Model Training

The proposed system trains its Convolutional Neural Network (CNN) model through the classification process using a GAN-

generated augmented dataset. Our CNN receives images through its deep convolutional layers as input to extract information

about spatial features including patterns and edges along with textures. By utilizing pooling layers the system decreases

dimensional input data for obtaining effective information that prevents over-fitting problems.

The implementation of fully connected layers optimizes the network learning capability in converting features into ultimate

probabilistic classifications for lesions (benign or malignant). The hidden layer incorporates Rectified Linear Unit = ReLU

activation functions to bring non-linearity thus allowing the model to perform additional calculations. The weighted sum

computation in first position yields predictions that need to be normalized using the softmax function as the final sequential

operation prior to classification.

A categorical cross-entropy loss function and Adam optimiser device the training of the model to determine label accuracy when

working with tasks that have sparse gradient distributions. During multiple epochs training we apply early stopping approaches to

prevent cases where the model shows good performance on training entries yet demonstrates poor results on validation entries.

The detailed training methodology creates a CNN model that achieves robust performance in differentiating skin lesions.

Document evaluation combined with site and database record comparison depends upon field identification procedures while also

requiring display of identified fields before moving onto result list creation followed by result display and document-site selection

methods.

A trained CNN receives real skin lesion images from the test suite to determine the accuracy of the proposed system. The new

configuration metrics report both accuracy assessments and precision levels along with recall test results combined with F1-score

and a measurement of the area under the ROC curve. Research teams evaluate the study results by comparing synthetic data

augmentation methods against other approaches. Researchers compare the outcome of a CNN which operates on augmented data

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input against the results of a baseline CNN which runs on the original dataset. The research findings demonstrate how synthetic

dataset utilization optimizes model operational effectiveness.

Rare Lesion Detection

Regular databases typically do not feature enough representative examples of rare skin lesion types leading to suboptimal

identification results. GAN requires precise training to synthesize low-frequency data samples which are underrepresented in

datasets. The addition of synthetic images of rare lesions to the CNN model generates optimal detection capabilities which build a

complete fair diagnostic system.

Workflow Integration

The proposed methodology forms an efficient skin cancer detection workflow which integrates all its fundamental components.

The GAN technology enables few-shot generators to produce synthetic skin images starting from preprocessed skin lesion images.

Both original data and its augmented version are supplied to CNN for classification. The system produces complete classification

results for lesions with concurrent performance metrics thus establishing itself as an effective method to detect skin cancer.

Figure 3 System Architecture

Figure 4 User interface workflow

●

User Interface: Acts as the primary interface for user interaction.

●

Upload Module:Allows users to upload skin lesion images for processing.

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●

Visualization Module:Displays results and metrics, providing insights into the classification.

●

Interpretability: Generates saliency maps and explanations for model decisions.

●

Report Generation:Produces a detailed summary of findings for further analysis.

IV. Results and Discussion

This section presents the evaluation of the proposed methodology for skin cancer detection using metrics such as accuracy,

precision, recall, and F1-score. Relevant tables and placeholders for figures are provided to illustrate the findings.

●

Dataset: ISIC 2020 Challenge Dataset

●

Types of Lesions: Melanoma, Basal Cell Carcinoma, Squamous Cell Carcinoma

Performance of CNN on Original and Augmented Datasets

The performance of the CNN model was assessed using two datasets: the original dataset and the augmented dataset, which

included GAN-generated synthetic images. Table 2 summarizes the evaluation metrics, including accuracy, precision, recall, and

F1-score.

Table 2: Performance Metrics of CNN Models

Dataset

Accu racy (%)

Precision (%)

Recall (%)

F1-Score (%)

Original Dataset Only

85.6

84.8

83.9

84.3

Augmented Dataset (with GAN)

91.2

90.7

89.8

90.2

Figure 5 Comparison of performance metrics

Figure 5: A bar chart comparing the accuracy, precision, recall, and F1-score of the CNN models trained on the original and

augmented datasets.

Explanation: The table and figure show that data augmentation with GAN-generated synthetic images improved CNN's

performance significantly across all metrics. This indicates better generalization and robustness.

GAN and CNN Loss Functions

●

GAN Loss Function:

GAN

= E[log(D(x))] + E[log(1 – D(G(z)))] L

= −E[log(D(G(z)))]

●

CNN Loss Function:

𝑳

𝑪𝒓𝒐𝒔𝒔𝑬𝒏𝒕𝒓𝒐𝒑𝒚

= −

i=1

∑

𝒚

𝒊

𝐥𝐨

𝐠

(ŷ

𝒊

)

(i) Confusion Matrix

Predicted Benign

Predicted Malignant

True Benign

120

True Malignant

130

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Figure 6 Confusion matrix

Rare Lesion Detection Improvement

Rare skin lesion types often pose challenges due to their underrepresentation in datasets. With synthetic data augmentation, the

CNN showed significant improvement in detecting rare lesions. Table 3 presents the detection rates for rare lesion types.

Table 3: Detection Rates for Rare Skin Lesion Types

Lesion Type

Detection Rate (%) -

Original Dataset

Detection Rate (%) - Augmented

Dataset

Melanoma

76.5

87.4

Basal Cell Carcinoma

79.3

89.6

Squamous Cell Carcinoma

72.8

85.9

Figure 7: A comparative bar chart showing the detection rates for rare lesion types before and after augmentation.

Explanation: The table and figure highlight the effectiveness of synthetic data in balancing the dataset, thereby enhancing the

model's ability to classify rare lesions.

Receiver Operating Characteristic (ROC) Curve Analysis

The Receiver Operating Characteristic (ROC) curve provides insights into the model's classification performance across various

thresholds. Figure 4 (Placeholder) shows the ROC curves for CNN models trained on the original and augmented datasets, with

the corresponding area under the curve (AUC) values.

 AUC (Original Dataset): 0.89

 AUC(Augumented Dataset): 0.94

Explanation: The ROC curves and AUC values indicate improved performance and decision-making ability of the CNN model

trained on the augmented dataset

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Table 4: Summary of Improvements

Metric

Original Dataset

Augmented Dataset

Improveme nt (%)

Accuracy (%)

85.6

91.2

+6.5

Precision (%)

84.8

90.7

+5.9

Recall (%)

83.9

89.8

+5.9

F1-Score (%)

84.3

90.2

+5.9

V. Conclusion

This work demonstrates an advanced approach for developing GAN-generated synthetic datasets alongside CNN applications for

skin cancer classification showing significant performance improvement. Analysis demonstrates a superior performance level for

machines trained with augmented data when detecting uncommon lesions in addition to improving model generalization

capabilities. Saliency maps implemented during analysis allowed researchers to access decision-making autonomy from the model.

Results validate the potential for synthetic data augmentation to advance medical image classification through increased

reliability and diversity in the future. Future work will enhance this system through Explainable AI applications alongside multi-

modal dataset utilization.

Future Scope

Through this system engineers can develop improved ways for detecting skin cancer alongside better medical imaging

procedures. When creating future versions researchers should work to develop explainable models which enhance

transparency for medical staff and patients. The combination of dermoscopic images with patient histories along with

genomic data enhances both effectiveness and assessment capability in the system. GAN models such as StyleGAN and

diffusion models offer potential structural enhancements to create better synthetic data which successfully represents

minority data types that commonly exist in rare complex lesion examples.

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