The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas

Table 2

Additional comparison for machine learning algorithms from the related work section.

Article title and author	Method	Accuracy	Summarization
Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network; Nugroho, Ardan Adi, et al [7]	CNN	0.78	Nugroho et al. investigated to create a skin cancer identification system for decision making. The proposed system was based on the convolutional neural network (CNN) algorithm, and it has three stages such as convolutional layer, pooling layer, and fully connected layer. The convolution layer applies the output function as a feature map from the image. Rectified linear unit (ReLu) used as an activating function. Pooling layer was used to reduce the size of the representation and to reduce the speed. This layer mainly gives the ability to recognize an object. Fully connected layer is used to transform the data dimension and to connect the previous layer to the next layer.; The results of this CNN model that uses input shape with the following parameters exhibit that the level of training accuracy is 80% and the testing accuracy is 78%; input shape size 90120-pixel, adam optimizer, learning rate 0.001, and number of epochs 50. Basal cell carcinoma disease was the most difficult to identify by the system, while actinic keratoses and intraepithelial carcinoma diseases are the most likely to be identified. However, the proposed model did not evaluate with any other model.
Recent advances in deep learning applied to skin cancer detection; Pacheco, Andre G. C., and Renato A. Krohling. [8]	n/a	n/a	This article is a summary of how machine learning and image processing can help dermatologists more rapidly identify skin cancers, in particular melanomas (the deadliest form of skin cancer). Due to the pressures created by increases in healthcare cost, lack of qualified professionals, and lack of access to relevant medical tools, cases of melanoma being diagnosed at a late stage have been going up. The article explores solutions to this problem and makes three major arguments–images run through machine learning algorithms (particularly models made up of a composition of methods of learning) can be at least as effective at diagnosis of skin cancers as dermatologists (assuming a good image is given)–these algorithms need to be able to work with clinical image data (i.e., from standard cameras), rather than medical imaging devices, and that there is a significant lack of data for testing and training, particularly when it comes to data with relevant metadata (patient age, race, diseases, etc.) associated with an image.; This article seeks to explore the basics of machine learning and how it can be applied to image processing, including examples of how it has already been applied in the field. As such, the main contribution to the field that this article has is as a compilation of works that have already been done at the intersection of machine learning and medical imagery. As such, the article has no new major contributions to add, as it is primarily derivative in nature, but is a good jumping-off point into the field of other works.
A convolutional neural network framework for accurate skin cancer detection; Thurnhofer-Hemsi, K., Domínguez, E. [9]	DenseNet201	0.95	Another analysis was performed on the HAM10000 dataset using a DenseNet201 neural network and image augmentation, demonstrating that it may be an effective model to use for this purpose, due to its high classification accuracies and low rate of false negatives.