Background: Automated intelligent systems for unbiased diagnosis are primary requirement
for the pigment lesion analysis. It has gained the attention of researchers in the last few
decades. These systems involve multiple phases such as pre-processing, feature extraction, segmentation,
classification and post processing. It is crucial to accurately localize and segment the
skin lesion. It is observed that recent enhancements in machine learning algorithms and dermoscopic
techniques reduced the misclassification rate therefore, the focus towards computer aided
systems increased exponentially in recent years. Computer aided diagnostic systems are reliable
source for dermatologists to analyze the type of cancer, but it is widely acknowledged that even
higher accuracy is needed for computer aided diagnostic systems to be adopted practically in the
diagnostic process of life threatening diseases.
Introduction: Skin cancer is one of the most threatening cancers. It occurs by the abnormal multiplication
of cells. The core three types of skin cells are: Squamous, Basal and Melanocytes. There
are two wide classes of skin cancer; Melanocytic and non-Melanocytic. It is difficult to differentiate
between benign and malignant melanoma, therefore dermatologists sometimes misclassify the
benign and malignant melanoma. Melanoma is estimated as 19th most frequent cancer, it is riskier
than the Basel and Squamous carcinoma because it rapidly spreads throughout the body. Hence, to
lower the death risk, it is critical to diagnose the correct type of cancer in early rudimentary phases.
It can occur on any part of body, but it has higher probability to occur on chest, back and legs.
Methods: The paper presents a review of segmentation and classification techniques for skin lesion
detection. Dermoscopy and its features are discussed briefly. After that Image pre-processing techniques
are described. A thorough review of segmentation and classification phases of skin lesion
detection using deep learning techniques is presented Literature is discussed and a comparative
analysis of discussed methods is presented.
Conclusion: In this paper, we have presented the survey of more than 100 papers and comparative
analysis of state of the art techniques, model and methodologies. Malignant melanoma is one of the
most threating and deadliest cancers. Since the last few decades, researchers are putting extra attention
and effort in accurate diagnosis of melanoma. The main challenges of dermoscopic skin lesion
images are: low contrasts, multiple lesions, irregular and fuzzy borders, blood vessels, regression,
hairs, bubbles, variegated coloring and other kinds of distortions. The lack of large training dataset
makes these problems even more challenging. Due to recent advancement in the paradigm of deep
learning, and specially the outstanding performance in medical imaging, it has become important to
review the deep learning algorithms performance in skin lesion segmentation. Here, we have discussed
the results of different techniques on the basis of different evaluation parameters such as
Jaccard coefficient, sensitivity, specificity and accuracy. And the paper listed down the major
achievements in this domain with the detailed discussion of the techniques. In future, it is expected
to improve results by utilizing the capabilities of deep learning frameworks with other pre and post
processing techniques so reliable and accurate diagnostic systems can be built.