A Deep Learning Framework for Early Detection of Skin Cancer: Implementing CNNs with Augmented Dermoscopic Images

Abstract

Skin cancer, particularly melanoma, poses a significant global health risk due to rising cases, high mortality rates, and delayed detection. This project introduces an automated detection system using image processing and a CNN classifier to differentiate malignant from benign lesions. By employing data augmentation, feature extraction, and preprocessing techniques, the model enhances early diagnosis, enabling timely and life-saving interventions

Introduction

Skin cancer is one of the most common cancers worldwide, and early, accurate detection is critical for effective treatment and improved patient outcomes. This project focuses on using machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNNs), to assist medical professionals in distinguishing between benign and malignant skin lesions. According to the WHO, millions of new melanoma and non-melanoma cases are reported annually, highlighting the need for faster and more reliable diagnostic tools.

The literature review emphasizes that deep neural network–based approaches are among the most effective methods for automated skin cancer detection, as they can analyze large datasets of dermatoscopic images with high accuracy.

The proposed methodology involves extensive image preprocessing (hair removal, glare and shadow reduction, and segmentation) followed by CNN-based classification. To address data imbalance and limited diversity, data augmentation and oversampling techniques are applied, improving model generalization and prediction accuracy.

Implementation uses a large, publicly available dataset of skin lesion images, following a standard computer-aided diagnosis pipeline: image acquisition, preprocessing, segmentation, feature extraction, and classification. CNN architectures with convolution, pooling, activation (ReLU), and classification layers are employed to learn discriminative features automatically.

The results demonstrate successful classification of benign, malignant, and non-cancerous cases, with all test cases passing as expected. Overall, the system shows that AI-assisted skin cancer detection can provide accurate, efficient, and reliable support for early diagnosis, reducing dependence on manual analysis and improving clinical decision-making.

Conclusion

This study shows that it is possible to accurately identify plant species by combining advanced machine learning methods withconventional morphological analysis. Toimproveclassification accuracy, the proposed model makesuse of high-resolution images of various plant parts, like leaves and flowers, in addition to environmental factors like the type of soil and the climate. The model, developed with Keras and TensorFlow in a CNN framework, was trained on a dataset of more than 10,000 samples and performed better than conventional methods with an accuracy of more than 95%.

References

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Copyright

Copyright © 2026 D. Venkateswarlu, Y. Sudhir Babu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.