An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

Abstract

Skin cancer is the most common deadly disease caused due to abnormal and uncontrolled growth of cells in the human body. According to a report, annually nearly one million people are affected by skin cancer worldwide. To protect human lives from such life‐threatening diseases, early identification of skin cancer is the only precautionary measure. In recent times, there already exist numerous automated techniques to detect and classify skin lesion malignancies using dermoscopic images. However, analyzing the dermoscopic images becomes an arduous task due to the presence of troublesome features such as light reflections, illumination variations, and uneven shape and dimension. To address the challenges faced during skin cancer recognition process, in this paper, we proposed an efficient intelligent automated system to detect and discriminate the dermoscopic images into malignant or benign. The proposed skin cancer detection model utilizes the HAM10000 dataset for evaluation. The dermoscopic images acquired from the HAM10000 dataset are initially preprocessed to enhance the quality of image and thus making it fit to train the classifier. Afterward, the most significant image patterns are extracted by the AlexNet architecture without any loss of detailed information. Later on, the extracted features are inputted to the proposed Improved Adaboost‐based Aphid–Ant Mutualism (IAB‐AAM) classification model to discriminate the images into malignant and benign categories. The proposed IAB‐AAM approach witnessed extensive enhancement in classification accuracy. The enhanced performance is attributed by integrating the AAM optimization concept with the IAB model. By comparing the performance of the proposed IAB‐AAM with other modern methods in terms of different evaluation indicators namely accuracy, precision, specificity, sensitivity, and f‐measure, the efficiency of the proposed IAB‐AAM technique is analyzed. From the experimental results, it is known that the proposed IAB‐AAM technique attains a greater accuracy rate of 95.7% in detecting skin cancer classes than other compared approaches.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…Vidhyalakshmi and Kanchana [84] used a GSO-optimized kernel random forest classifier based on transfer learning for classifying 12 different skin cancer types, leveraging ImageNet and various CNN architectures. Renith and Senthilselvi [85] proposed a model using an improved Adaboost-based Aphid-Ant Mutualism classification model. Lastly, Desale and Patil [86] introduced an optimized vision transformer approach for classifying skin tumors using a self-sparse watershed algorithm.…”