# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 2 Literature Review  
It has been discovered that an expert dermatologist typically follows a process that begins with visual inspection of potential lesions with the unaided eye, then dermoscopy, which magnifies lesions under a microscope, after which biopsy can be undertaken. This approach is subjective and prone to errors. Automated approaches like the use of AI are desired to enhance accuracy and efficiency in diagnosis. AI encompasses the discipline of crafting intelligent machines, particularly sophisticated computer programs. An alternative definition of AI involves the emulation or recreation of cognitive processes through computer systems, endowing them with the capability for logical reasoning and behavior akin to humans. The prevalence of AI in scholarly discourse escalated after the 1950s. Industries spanning communication, information technology, healthcare, agriculture, logistics, education, and aviation are among the many domains that leverage AI [11; 12; 13; 14; 15]. Transfer learning in machine learning refers to the practice of repurposing a model that has previously been trained on a distinct problem. This approach enables a computer to leverage its comprehension of one task to enhance its ability to make generalizations about another. Transfer learning involves employingthe early and intermediate layers of a model, while only retraining the latter layers. This technique proves valuable in capitalizing on the labeled data from the original task on which the model was trained [16]. The study conducted by [17] employed a MobileNet model pretrained on around 1,280,000 images from the 2014 ImageNet Challenge. This model was subsequently fine-tuned using transfer learning on a dataset comprising 10,015 dermoscopy images from the HAM10000 dataset. The outcome of this approach yielded an impressive overall accuracy of 83.1%. [PERSON] et al. [18] employed a pretrained architecture called ResNet50 for classifying skin lesions as either melanoma or nevi. The model they proposed attained sensitivity and specificity rates of 77.9% and 82.3%, respectively. [PERSON] et al. [19] employed a pretrained model known as Inception-v3 for the classification of skin lesions. They expanded the testing process, and the resulting classification model achieved an accuracy of 71.2%. [PERSON] used transfer learning methods with the HAM10000 dataset. The pretrained model chosen, PNASNet-5-Large, has an accuracy of 76%. The HAM10000 dataset is uneven, with a notable difference in the overall amount of photos for each class, which is crucial to highlight. It is difficult to generalize the characteristics of the lesions because of this imbalance [20]. A customized CNN model was created by [PERSON] [21] and trained on the HAM10000 dataset, When it came to categorizing the skin lesions in the dataset, the model had a 78% accuracy rate. In [22], authors utilized a pre-trained MobileNet model to build their own model. They conducted their research using the HAM10000 dataset. Impressively, they achieved a categorical accuracy of up to 80.81%. In [23], authors proposed a method to improve the quality of images for extracting Regions of Interest (ROIs). They utilized a deep residual model for the classification of these images. The results showed that their system achieved an accuracy of 85.5%. [PERSON] et al. [24] proposed utilizing a pre-trained CNN (ImageNet) to distinguish between healthy and melanoma images by extracting high-level feature representations. In a study by [25], a deep learning-based classification model was developed for pigmented skin lesions. The model's training employed datasets encompassing six classes, including malignant tumors such as malignant melanoma and basal cell carcinoma, as well as benign tumors like nevus, seborrheic keratosis, senile lentigo, and hematoma/hemangioma. The researchers utilized a Faster Region-Based Convolutional Neural Network (FRCNN) for training and evaluating the dataset. The test outcomes demonstrated an accuracy of 86.2% in classifying skin lesions across the six categories.