Skin Cancer Detection using Convolutional Neural Network
Keywords:
Skin Cancer Detection, Deep Learning, Convolutional Neural Network (CNN), Medical Image Analysis, Dermoscopic Images, Benign and Malignant Classification, Artificial Intelligence, Image Classification, Computer-Aided Diagnosis, Early Disease DetectionAbstract
Skin cancer is one of the most common and life-threatening forms of cancer worldwide, making early and accurate
diagnosis essential for improving treatment success and reducing mortality rates. Conventional diagnostic
methods, including visual examination by dermatologists and traditional image processing techniques, are often
subjective, time-consuming, and dependent on clinical expertise, which may lead to delayed or inaccurate
diagnosis, particularly during the early stages of the disease.Recent advancements in artificial intelligence (AI)
and deep learning have significantly enhanced the field of medical image analysis. Among these technologies,
Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in automatically extracting
complex image features and accurately classifying skin lesions without requiring manual feature engineering.
This project presents a CNN-based automated skin cancer detection and classification system capable of
distinguishing benign and malignant skin lesions from dermoscopic images.The proposed system utilizes a
publicly available dermoscopic image dataset, where images undergo preprocessing techniques such as resizing,
normalization, and data augmentation to improve model performance and generalization. The processed images
are then fed into a CNN architecture consisting of convolutional, pooling, activation, and fully connected layers
for feature extraction and classification. The trained model effectively identifies subtle visual patterns associated
with different skin lesion types, thereby improving diagnostic accuracy while minimizing human error.
The proposed approach aims to provide a fast, reliable, and cost-effective computer-aided diagnostic system that
can assist dermatologists in early skin cancer screening and clinical decision-making. By leveraging deep
learning techniques, the system has the potential to enhance diagnostic efficiency, reduce workload, and improve
patient outcomes through timely detection and treatment of skin cancer.
