Abstract:
Among lesions having high mortality rate, skin lesion is at top of the list. The
patient‟s life could be rescued through early diagnosis. The manual
identification period could be prolonged, as different categories of skin cancer
have high similarities between their structure, color, and size which could lead
to misclassification. An automatic and robust system is crucial for the timely
and accurate categorization of skin cancer. In the last few decades, deep
learning emerges as revolutionizing field, especially in the area of medical
imaging. Most state-of-the-art work adopted transfer learning and ensemble
learning based techniques for this problem, in which models used for TL and
EL are designed for different types of problems and mostly trained on the
huge amount of data which sometimes don‟t perform well for distinctive skin
cancer problems due to very challenging datasets. This work proposed novel
architecture based on a deep learning technique, which is designed especially
for skin cancer classification problem. For learning, this method used
dermoscopic images, which are excellent quality images captured through a
high-level magnifying device, to obtain clear insights of skin containing
cancer cells. The proposed architecture‟s main contribution includes,
enhancing performance by embedding inception residual (IR) blocks in which,
inception block is warily designed with several parallel layers that are merged
together. Moreover, residual connections are established in each block to learn
from low and high-level features concomitantly. These connections accelerate
the learning process along with coping with the vanishing gradient problem.
Deployment of the attention unit in the network boosted performance by
suppressing the value of noise-containing features and assigning maximum
value to important and relevant features during the learning process. Reducing
the number of parameters by keenly optimizing the size and no of filters is
another milestone attained through suggested model. The presented
architecture achieved promising results by attaining 91.63% accuracy, 91.60%
x
precision, 91.60% sensitivity, 91.52% specificity, and 91.60% f1-score on the
ISIC-19 dataset.
