# Skin Lesion Classification Using a Deep Ensemble Model
## 2 Literature Review  
Researchers had undertaken an exhaustive exploration of various algorithms employed for feature selection and melanoma prediction, as elaborated in [10]. These predictive frameworks were intricately devised for diagnosing melanoma, incorporating real-world data-gathering approaches. The development of the model hinged on a series of guidelines formulated in collaboration with domain experts. Noteworthy is the substantial investment necessitated by the need for swift image processing and the acquisition of nuanced insights to fine-tune the optimal rule set. Interestingly, many researchers chose to incorporate deep learning methodologies into their system. In their study [11], the authors presented a novel concept focused on employing a one-class deep neural network learning framework. This innovative approach was contrasted with alternative classifiers such as Isolation Forest, Gaussian Mixtures, and OC-SVM techniques. Following a comprehensive assessment of these methodologies, the authors determined that the Isolation Forest technique was the most suitable for the dataset under investigation. This method operates on the premise that features linked to anomalies diverge notably from those of normal samples. Central to this approach is the creation of an ensemble consisting of isolation trees, where anomalies exhibit significantly shorter average path lengths. In their study, the researchers devised an automated system designed to analyze skin lesions, with a particular emphasis on identifying melanoma and performing semantic segmentation [12]. Utilizing advanced deep learning techniques and publicly available dermoscopic images, the system significantly improved melanoma detection. Initially, the image classification utilized a deep convolutional neural network, integrating feature extraction methods. Subsequently, these extracted features were inputted into a random forest classifier. By combining the outputs from both classifiers, the system achieved notable performance metrics with a precision of 0.81, an accuracy of 80.3%, and an AUC score of 0.69. Furthermore, segmentation utilized a convolutional-deconvolutional architecture, resulting in a dice coefficient of 73.5%, illustrating precise delineation of segmented regions. In the study described in [13], a five-layered CNN with the PH2 dataset was utilized. The CNN proposed in the study demonstrated remarkable performance, achieving approximately 95% in accuracy, 94% in sensitivity, 97% in specificity, and a perfect AUC score of 100% on the test set, as evaluated by four essential performance metrics. The authors of [14] presented a novel deep learning-based method designed to enhance the accuracy of classification systems. The method employs a multi-scale decomposition model in the preprocessing phase, utilizing texture as input for the CNN to eliminate separate texture extraction and feature selection. The experimental findings demonstrate that the proposed approach attains superior accuracy when contrasted with other established algorithms and methodologies documented in the existing literature. In another research study [15], CNN was employed to detect malignant and benign lesions utilizing the ISIC2018 dataset, comprising 3533 images on a variety of skin lesions, ranging from benign and malignant tumors to nonmelanocytic and melanocytic growths. Initially, the images underwent enhancement using ESRGAN. During preprocessing, augmentation, normalization, and resizing techniques were applied to the images. The CNN method was utilized to classify the skin lesion images, based on aggregated results from multiple iterations. Furthermore, various transfer learning models, including ResNet50, InceptionV3, and Inception ResNet, underwent fine-tuning. The custom CNN model achieved an accuracy of 83.2%, which was comparable to the pre-trained models: Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%).