Abstract:
Skin cancer is a disorder that arises from changes in healthy skin cells that give them the ability
to become malignant. Due to a rise in predominance over the past ten years, it is currently
placed among the top ten malignancies in terms of frequency. Patients who are unaware of skin
cancer may not be encouraged to seek medical attention for minor skin discoloration because
many people lack the knowledge required to notice it. One can lessen and manage the
detrimental effects of skin cancer with an accurate diagnosis and prompt, efficient therapy.
Investigating skin cancer lesions can be difficult due to their comparatively similar forms,
complex expression of the disease, and susceptibility to subjective diagnosis. The obtained
features from the multiple EfficientNet model tiers are combined using a feature fusion approach
to solve this challenge. Therefore, in this study, a system was developed that could detect and
classify skin cancer lesions into benign and malignant automatically by using a feature fusion
strategy and EfficientNet algorithm with a transfer learning method. The image dataset was
collected from a public dataset that is available on Kaggle and the total dataset used is 27560
from both classes benign and malignant. Pre-processing the skin lesions, extracting features
using a pre-trained EfficientNet, feature concatenation, and classifying using deep learning
EfficientNet algorithm are the primary components of this research. The study method was tested
and yielded average results of 93.4% accuracy, 92.3% precision, 94.8% recall, 92.1%
specificity, and 93.5% f1-score, respectively as well as confusion matrix achieved 1269(92.00%)
true positives, 109(8.00%) false positives, 72(5.00%) false negatives, and 1306(95.00%) true
negatives
