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Melanoma skin cancer and nevus mole classification using intensity value estimation with convolutional neural network

Ashafuddula N. I. Md., Islam Rafiqul

Computer Science

2023

T. 24 (3)

277--296

Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which can causes melanoma skin cancer. However, detecting and classifying melanoma and nevus moles at their immature stages is difficult. In this work, an automatic deep-learning system has been developed based on intensity value estimation with a convolutional neural network model (CNN) for detecting and classifying melanoma and nevus moles more accurately. Since intensity levels are the most distinctive features for identifying objects or regions of interest, high-intensity pixel values have been selected from extracted lesion images. Incorporating those high-intensity features into CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used five-fold cross-validation. The experimental results showed that superior percentages of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) were achieved.

A novel melanoma detection model: adapted K-means clustering-based segmentation process

Sukanya S. T., Jerine

Bio-Algorithms and Med-Systems

2021

Vol. 17, no. 2

103--118

Objectives: The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids. Methods: This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection. Results: Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively. Conclusions: From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.