# Skin Lesion Classification Using a Deep Ensemble Model
## 2 Literature Review
In the research cited in [16], a deep learning model was developed to detect skin cancer using the HAM10000 dermoscopicimage database that includes 513 BCC, 790 benign, 327 actinic and intraepithelial carcinoma (AKIEC), and 115 dermatofibroma events. In this research, a CNN was created to detect benign and malignant groups. AlexNet was utilized as the pre-trained model. This model directly processes raw images, learning crucial features for classification without lesion segmentation or manual feature extraction. It achieved an accuracy of 84%, specificity of 88%, an area under the receiver operating characteristic (ROC) curve of 0.91, and sensitivity of 81%, with a confidence score threshold of 0.5. In this paper [17], deep learning was utilized to identify malignant and benign tumors using skin images of the RGB channel. A combination of lesion segmentation and classification was employed to detect malignant and benign tumors. For segmentation, U-Net was utilized, and an algorithm was used for classification. Other researchers have developed a CNN model for accurately identifying skin cancer [18]. AlexNet was employed with the HAM10K dataset that is highly imbalanced across classes, which typically results in lower training accuracy. Moreover, a novel activation function was developed to mitigate the vanishing gradient problem and tested across multiple benchmark architectures. It provided superior accuracy compared to existing activation functions with an accuracy of 98.20%, and precision, recall, and F-score of 98.20%, outperforming current models. Another study [19] introduced a deep learning model for detecting skin lesions at the benign and malignant stages utilizing transfer learning. The method built upon a pre-existing VGG16 architecture, enhancing it by incorporating additional layers. Their integration aimed to enhance accuracy in the classification task. The evaluation utilized a Kaggle dataset, implementing data augmentation methods to diversify the input data and bolster model robustness. Numerous hyperparameters were explored, encompassing batch sizes ranging from 8 to 128, varied epochs, and different optimizers. The optimized model demonstrated exceptional performance, attaining an accuracy of 89.09%. This achievement was realized with a batch size of 128, utilizing the Adam optimizer and undergoing training for 10 epochs. Notably, these results surpassed existing methodologies. The system proposed in [20] detected skin cancer from skin images by first applying a median filter to reduce noise. Following this, the images were segmented using the Mean Shift segmentation technique. Following segmentation, feature extraction was conducted on the segmented images, with emphasis on extracting features such as GLCM (Gray-Level Co-occurrence Matrix), Moment Invariants, and GLRLM (Gray-Level Run Length Matrix). These extracted features were subsequently classified using multiple techniques, including Support Vector Machine (SVM), Probabilistic Neural Networks (PNNs), and random forest (RF). Notably, the combined SVM+RF classifier emerged as the most successful approach, yielding the highest performance. The paper [21] introduced a combination of faster region-based CNN and fuzzy k-means clustering (FKM) to segment melanoma. The method was tested on several clinical images to aid dermatologists in the early diagnosis of this serious and potentially life-threatening condition. Initially, the images in the dataset underwent preprocessing to address issues such as noise and uneven illumination, which aims to improve the clarity of visual data. Following this, the faster R-CNN algorithm was utilized to produce a fixed-length feature vector from the images. Following this step, the fuzzy k-means (FKM) algorithm was utilized to delineate the boundaries of melanoma-affected skin regions, which may vary in size and shape. The efficacy of this methodology was evaluated using three well-established datasets: ISBI-2016, PH2, and ISIC-2017.