Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

Abstract

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

Semi-supervised skin cancer diagnosis based on self-feedback threshold focal learning

“…Gajera et al [ 20 ] proposed an ensemble CNN feature fusion and sparse autoencoder based framework to improve melanoma classification performance; Sethanan et al [ 21 ] introduced a Skin Cancer Classification System combining image segmentation, CNNs, and a dual Artificial Multiple Intelligent System, resulting in over 99.4% accuracy in skin cancer classification. Bassel et al [ 22 ] developed a stacking classifier method using Resnet50, Xception, and VGG16 that reached 90.9% accuracy for melanoma and benign skin cancer detection. These contributions signify progress toward more accurate and universally applicable skin cancer diagnostic methods.…”

Skin Cancer Detection Using Deep Learning—A Review

“…Bassel et al [44] proposed a hybrid deep learning approach based on the Stacked CV method trained on the ISIC 2019 for classifying skin cancer. Bassel et al [44] trained proposed Stacked CV method in three levels by deep learning, SVM [45], RF [46], NN [47], KNN [48], and logistic regression methods as shown in Kousis et al [49] trained eleven popular CNN architectures using HAM10000 dataset for classifying skin cancers into seven categories, actinic keratoses, intraepithelial carcinoma/Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines/seborrheic keratoses and lichen-planus-like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (NV), vascular lesions (angiomas, angiokeratomas, pyogenic granulomas, hemorrhage, vasc). Among the eleven CNN architecture configurations, DenseNet169 [21] produced the best results and achieved an accuracy of 92.25% and an F1-score of 0.932, which outperforms existing state-of-the-art efforts.…”

“…Bassel et al [44] Pre-trained models were used for extracting features. Stacked CV techniques consisting of five different classifiers were used for the classification of skin cancer images.…”

Malware Detection Using Deep Learning and Correlation-Based Feature Selection

“…Some previous studies utilized machine learning (ML) algorithms, which can make decisions after learning from the data templates. Machine learning is the concept of minimizing human intervention in computing systems [13]. Through the use of computer learning methodologies and experience or previous data, machine learning predicts decisions.…”