# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 2 Related Work
Furthermore, the authors introduced an artificial intelligence technique for skin cancer detection that employed image processing and a deep neural network [15]. The approach included segmenting the affected region, extracting its features using image processing, and utilizing a Convolutional Neural Network for prediction. The training and testing accuracy achieved were 93.77% and 89.5%, respectively. Finally, researchers presented an adaptable federated machine learning model for diagnosing various skin disorders. This model incorporated Dermoscopy and global intelligence (server) and exhibited improved accuracy in diagnosing skin conditions [16]. [PERSON] and [PERSON] [17] proposed an automated method for detecting skin cancer, specifically melanoma, from photographs of affected skin regions. Their aim was to achieve higher precision by utilizing a small set of features. The images were initially divided into the relevant lesions, and then 15 features were extracted using techniques from image processing and computer vision. To implement the system, they employed a nonlinear Support Vector Machine (SVM) with a Gaussian radial basis function (RBF) kernel. The study involved a dataset of 200 photos, and the results showed that only six criteria were necessary for accurately identifying melanoma. The system achieved an accuracy of 86.67% using the optimal parameters. [PERSON] et al. [18] introduced a hybrid approach that combined a Convolutional Neural Network (CNN) with two classical machine learning techniques, specifically K-nearest neighbors (KNN) and Support Vector Machines (SVM). They collected a dataset of 640 lesion images from the International Skin Imaging Collaboration (ISIC) archive, using 512 images for training and the remaining 128 images for testing. The CNN architecture comprised 9 layers, including 3 convolutional layers, 3 max pooling layers, 2 dropout layers, and a fully connected layer for prediction. Various preprocessing techniques, such as the Otsu method, filters, and the morphological snake's method, were employed. The final classification involved all three algorithms, with a majority vote. This hybrid approach achieved an accuracy of 88.4%. Specifically, the CNN achieved the highest accuracy of 85.5% individually, followed by the SVM with a second-best accuracy of 71.8%. [PERSON] et al. [19] devised a method to classify universal skin diseases by fine-tuning the VGG16 and VGG19 architectures for training their networks. Their network attained a Top-1 classification accuracy of 60.0% and a Top-3 classification accuracy of 80.3%. These outcomes demonstrated a substantial improvement compared to existing methods in their experiments. The researchers further advocated for the adoption of a similar approach to enhance classification outcomes in skin disease detection. [PERSON] et al. [20] developed their own CNN that encompassed preprocessing, feature extraction, and classification functions. They employed a dataset of 170 nondermoscopic images obtained from the digital image archive of the Department of Dermatology at the University Medical Center Groningen (UMCG). To enhance the training process, the dataset was augmented, resulting in a total of 6120 original and synthesized images. Various preprocessing techniques, including illumination correction, mask generation, and Gaussian filtering, were applied. The CNN architecture consisted of an input layer, followed by alternating layers of convolution and max-pooling, culminating in a fully connected layer. The objective was to classify between two classes, melanoma and nevus. The proposed model achieved an accuracy of 81% with a specificity rate of 80%. [PERSON] et al. [21] conducted a study where pre-processed images from a medical database were employed as input for three different machine learning algorithms: K-nearest neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). The objective of the classification task was to assign images to either the cancerous or noncancerous class.