Abstract:
The complex feature characteristics and low contrast of cancer lesions, a high degree of
inter-class resemblance between malignant and benign lesions, and the presence of various
artifacts including hairs make automated melanoma recognition in dermoscopy images
quite challenging. To date, various computer-aided solutions have been proposed to identify
and classify skin cancer. In this paper, a deep learning model with a shallow architecture is
proposed to classify the lesions into benign and malignant. To achieve effective training
while limiting over fitting problems due to limited training data, image preprocessing and data
augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is
employed, which adds efficiency to our study by reducing the overall training time and space
complexity significantly. Our proposed shallow convolutional neural network (SCNN_12)
model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model
was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of
0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out
exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the
robustness the model is evaluated on noisy data to examine the performance when the
image quality gets corrupted. This research corroborates that effective training for medical
image analysis, addressing training time and space complexity, is possible even with a light weighted network using a limited amount of training data.
