An Intelligent Algorithm for Skin Cancer Detection

Nowadays, computer vision as an interdisciplinary field is growing in different areas such as medical, electronics, etc. In the field, detection and particularly image segmentation is an essential task in which is difficult to find the appropriate one based on the application. In this paper, a new algorithm is proposed to segment the lesion from background. The algorithm is based on log edge detector with iterative median filtering. We have tested our algorithm on 20 dermoscopic images and compare the lesion detection results with those manually segmented by dermatologists. The experiments represent the effectiveness of proposed algorithm.


Introduction
Skin cancer is determined by the abnormal and uncontrolled spread of cells which make up the skin. It is classified as benign or malignant. Malignant melanoma as a deadliest skin cancer tumor has a probability of metastasis and grows rapidly to other organs. Thus, the quick detection of melanoma is of extreme importance [1]. Investigations have demonstrated that the cure rate is approximately 100% if it is recognized and treated early [2]. For this reason, the automatic or semi-automatic interpretation of results became unavoidable [1].
Advances in clinical dermoscopy have considerably contributed to improve diagnosis and reduce risks. It entrusts 10% -27% higher sensitivity to diagnosing melanoma by dermatologists [3] [4]. Hence, development of dermoscopic diag-How to cite this paper: Nazerzadeh, A., Houshyar, A.N. and Jahed, A. (2020) An Intelligent Algorithm for Skin Cancer Detection. Intelligent Control and Automation, 11, 25-31. https://doi.org/10.4236/ica.2020.111003 Intelligent Control and Automation nosis still increasing in subject over the last years. Different methods have been developed and tested on a wide range of tasks to assist the system designer to choose the most appropriate approach. Clinical outcome of melanoma can potentially be ameliorated by increasing the accuracy of tumors localization during the treatment. Hence, the risk of mortality caused by late diagnosis can be reduced if the set-up uncertainties are minimized. One of the particular uncertainties in the system is determining the borders between tissues to find the features for accurate detection. The border detection is an important task because it provides the basis of extracting the features of lesion such as size and symmetry axes, and most conversant dermoscopic features, such as radial streaming and pseudopods. Since the transition between skin and lesion is almost of low contrast, and memberships sin lesion boundary are intrinsically fuzzy, the exact determination of borders is a difficult task In this study, we place the concentrate on determining the lesion borders. Our approach is particularly appealing and differs from others as it is robust in presence of hair and noise. Our motivation to perform segmentation of skin cancer images has been to facilitate the robust algorithm for automatic tracking of melanoma in detection systems which can delineate the lesions at risk.
Experiments and comparison results of more than 30 skin lesion images illustrate that the proposed algorithm results are very close to borders traced manually by a dermatologist, even for the images with high asymmetric lesions, weak edges and artifacts.
The remainder of the article is organized as follows: a short review on previous studies of skin cancer segmentation is presented. Then, the proposed algorithm for segmenting the melanoma from the images is explained in detail. Third, the experimental results are carried out on RGB images to evaluate the validity of our algorithm. The results are compared with the traditional algorithm to demonstrate the superiority of proposed algorithm. Finally, the article is concluded with discussion.

Previous Studies
In the past, several approaches have been proposed for segmentation of skin   [10]. In another study [11], the improved fuzzy clustering algorithms and its application in brain image segmentation was investigated and they concluded that their algorithm could accurately segment brain

Proposed Methodology
As mentioned before, lots of skin cancer segmentation algorithms have been presented in the literature to help dermatologists in accurate detection of melanoma. However, the noise and hairs affect significantly on segmentation precision and also the main purpose is to achieve the algorithm which segments the lesions in either malignant or benign lesion images.
Motivated by these issues, in this paper we propose an algorithm for skin le-

Results and Discussion
In this section, the results are discussed, and the validity of the proposed algorithm is investigated. To quantitatively evaluate the performance of the algorithm, experiments are carried out on thirty RGB images which have been segmented manually by a dermatologist. These manual segmentations are treated as ground truth. These images have been collected from the Cancer Council NSW and other referenced papers. The obtained images will then be compared with the ones from Log edge detector algorithm to demonstrate its superiority. Figure 1 shows a sample of a ground truth image. We validate our algorithm by comparing its statistical results with the ground truth as well as the results from the Log edge detector. Figure 2 shows the sample border detection results. The proposed algorithm demonstrates effective border lines.    The images are polluted with the additive noise of level 5% (SNR = 26 dB) to make higher noisy image and to evaluate the performance of the proposed algorithm. Figure 4 shows the results of segmented image showed earlier in Figure 2.
Additionally, to validate the algorithm, the Border Error (BE) and SNR between the proposed algorithm and Log edge detector algorithm are computed.
The percentage border error is computed as,    The experimental results demonstrate that proposed algorithm performs better than Log edge detector.

Conclusion
In this paper, we presented a generic framework for automatic skin lesion segmentation based on Log edge detection algorithm. Experiments were performed on 30 images with known ground truth obtained by manual detection of borders by a dermatologist. Our results show that the proposed algorithm is more efficient in detecting the edges than Log edge detection algorithm. The experiments indicate that the obtained results from our proposed algorithm are very close to that obtained by human.