Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for
the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by
ophthalmologists takes time to recognize the PSLs. Therefore, several “computer-aided diagnosis
(CAD)” systems are developed by using image processing, machine learning (ML), and deep learning
(DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex
features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for
the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based
on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus
on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeezeand-
excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet
architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to
validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we
have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we
collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the
experiments show that the suggested approach outperforms compared techniques in many cases.
The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of
97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models.
The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of
PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and
improve it. |
