The identification of some characteristics, such as symmetry, size, broths, and the presence and distribution of color features (but also blue-white areas, atypical pigmented networks, and globules) is essential for the diagnosis of skin lesions [7]; however, it is a complex and time-consuming procedure that shows a strong dependence on the subjectivity and experience of the physician. These problems necessitate the development of computer-aided diagnostic systems (CAD systems), which generally involve several steps for the analysis and classification of dermoscopic images of skin lesions [8]: preprocessing, segmentation, feature extraction, and classification using ML and DL approaches. Preprocessing aims to attenuate artifacts in the images, which are mainly due to the presence of hairs and marker marks on the lesions, while the segmentation phase aims to isolate the lesion from the surrounding skin and thus extract clinically relevant features. Several solutions have been proposed for these two phases by researchers [9; 10; 11; 12; 13; 14], many of which are laborious and require the training of additional machine and deep learning models. The feature extraction step can be manual [15] or can be automated using machine learning algorithms. Manual feature extraction in the case of skin lesion classification is based on the methodologies devised by dermatologists for skin cancer diagnosis and, in particular, the ABCDE rule of dermatology. Using machine learning methods, learned features are derived automatically from the datasets and require no prior knowledge of the problem. A variety of approaches are also possible for the final stage of classification: from classical machine learning approaches to state-of-the-art methodologies based on deep convolutional neural networks. Melanoma detection can be understood as an anomaly detection problem in skin lesions because melanomas represent anomalies (shape, size, color, etc.) in the nevi population. In this paper, we propose an anomaly detection method that involves only the manual and automatic feature extraction and classification steps. Specifically, we develop a custom CNN that, in addition to the dataset of melanoma and nevi images, is trained on additional handcrafted texture features extracted from whole of dermoscopic images. This avoids the segmentation step and could capture some aspects of pathology related to the tissue surrounding the lesions. In addition, no preprocessing is performed on skin lesion images, which reduces the working time. The main contributions of this paper are:  
1. A custom convolutional neural network model with added handcrafted features is proposed that classifies melanomas and nevi accurately without the need for image preprocessing and segmentation. 2. On the same test dataset, the performance of the proposed model exceeds that of the most widely used pre-trained models for skin lesion classification (ResNet50, VGG16, DenseNet121, DenseNet169, DenseNet201, and MobileNet). 3. The execution time required by the proposed model to execute the output results is much less than that required by the pre-trained models tested. 4. The proposed model achieves better accuracy on the ISIC 2016 dataset than other existing deep learning models. 5. A brief interpretability analysis of the proposed model is performed. The paper is organized into the following sections. Related work in the areas of skin lesion classification and anomaly detection is reported in Section 2. Section 3 discusses the main challenges and opportunities of skin cancer detection. Section 4 presents the materials used for the present work, namely public datasets of dermoscopic images. Section 5 shows the workflow of the proposed methodology, including the extraction of handcrafted features from dermoscopic images and the construction and training of the CNN network for the melanoma-nevi discrimination task. Section 6 provides details on the settings of the experiments and the creation of the datasets used in training and testing the model.