| | Article title and author | Method | Accuracy | Summarization |
| M-skin doctor: A mobile-enabled system for early melanoma skin cancer detection using a support vector machine
Aleem et al. [3] | SVM | 0.80 | Aleem et al. published an article introducing a mobile-enabled cancer detection system for early melanoma skin cancer using a support vector machine (SVM).
The proposed system can be identified as three main steps: preprocessing, segmentation, and feature extraction and classification. In the preprocessing step, image quality was improved by removing noise using the Gaussian function. In the segmentation step, the grab cut technique was used to split the image. In the feature extraction and classification step, meaningful features such as mean, standard deviation, and perimeter were extracted. They mainly choose histogram and ABCD features proposed by the ABCD rule. The SVM algorithm was applied as a classification technique. SVM algorithm provides good classification results in real-time smartphones. Even though the model has been only applied for skin melanoma, this application can be extended to other skin diseases (eczema and skin rashes). Its sensitivity and specificity rates are 80% and 75%. However, it would be worthwhile to evaluate the proposed system with a different algorithm such as CNN.
The idea of using smartphone apps as cancer detection tools is explored, including the fact that at least 40 apps are already out that claim to do so. These tools can be harmful as they may not actually be using any sort of detection and may just be apps to track sizes of the lesion, etc., and do not have the typical protections in place that meet the requirements of medical information (HIPPA). |
| Melanoma detection byanalysis of clinical images using a convolutional neural network
Esfahani et al. [4] | CNN | 0.81 | Clinical images (though not from a dermoscopy) were preprocessed to remove noise and illumination effects and fed into a convolutional neural network trained on many samples. |
| Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks.
Tschandl et al. [5] | CNN | 0.735 | Tschandl et al. explored how CNN achieves professional-level accuracy in diagnosing pigmented skin cancer; however, most common types of skin cancers are nonpigmented and hard to diagnose. Thus, the author expected to compare the accuracy of a CNN-based classifier on the diagnosis of nonpigmented skin cancer with that of physicians with different levels of experience in this study. The proposed system can be identified as two main steps, such as neural network diagnoses and human rating.
In the neural network diagnosis step, the first CNN-based classification model was trained on thousands of dermoscopic and close-up images of lesions removed at a primary skin cancer clinic for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with the results from 95 human raters who were medical professionals with expertise in different areas of dermatology. CNN has achieved a higher percentage in nonpigmented skin cancer diagnosis than beginner and intermediate level medical personnel but not expert medical personnel. However, the presented model has a lower accuracy than other recent publications. This may be due to the small sample size with different classes, and using a large sample set could resolve this problem and improve the accuracy. Also, the proposed model did not evaluate with any other model. | The impact of patient clinical information on automated skin cancer detection
Pacheco et al. [6] | ResNet-50 | 0.788 | The article compares various methods of training a model to recognize cancer in images and considerations that must be made when doing so, particularly when it comes to unsupervised training. The most interesting point is that if control data images are taken on a different camera or dermoscopy, the model may end up learning to pick the images on the subtle differences in the image related to a given model of the device, not the cancer itself. This article goes into detail about one potential data source for images to be used for training, the International Skin Imaging Collaboration. | | ResNet-101 | 0.757 | | GoogleNet | 0.779 | | MobileNet | 0.762 | | VGGNet-13 | 0.746 | | VGGNet-19 | 0.750 |
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