A Collaborative Biomedical Image-Mining Framework along with Image Annotation

Kaur, Kamalpreet; Ada, Ada

doi:10.5120/20398-2699

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…Due to these elements, the detection of lesion area with high accuracy is tough, so, it might result in a decrease of the exactness, and the processing time can be elevated. To refine the quality of the scan, cropping image [37], contrast intensification [26,38], image resizing [29,39], RGB to Greyscale imaging, morphological image processing, filters application [24], Gaussian filter [29] and color quantization, are applied to the scan which makes the image segmentation less challenging. Therefore, it is required to apply a few preliminary processing for the ejection of these elements.…”

Section: B Preprocessingmentioning

confidence: 99%

“…In supervised classifications the dataset should be provided with appropriate labels of melanoma and non-melanoma. The previous literature showed the Support Vector Machine [29,38], K-NN [37,52], Naïve Bayes [24,60], Artificial Neural Networks [24,52,[61][62][63], Multilayer Perceptron [52,62], Logistic Model Tree [20], Hidden Naive Bayes [44] Decision Trees [23,64,65], Proximal Support Vector Machine (PSVM) and Active Support Vector Machine (ASVM) [28] are the supervised machine learning algorithms used for automatic diagnosis of melanoma. Furthermore, the techniques like Clustering [63] and fuzzy C-means are unsupervised machine learning algorithms used for diagnosis purpose.…”

Section: Classificationmentioning

confidence: 99%

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Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Asif¹,

Fatima²,

Anjum³

et al. 2019

ijacsa

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Melanoma is a type of skin cancer, one of the fatal diseases that appear as an abnormal growth of skin cells and the lesion part often looks like a mole on the skin. Early detection of melanoma from skin lesion by means of screening is an important step towards a reduction in mortality. For this purpose, numerous automatic melanoma diagnosis models based on image processing and machine learning techniques are available for computer-based applications (CBA) and smartphone-based applications (SBA). Since, the smartphones are available as most accessible and easiest methods with built-in camera option, SBA are preferred over CBA. In this paper, we explored the available literature and highlighted the challenges of SBA in terms of execution time due to the limited computing power of smartphones. To resolve this issue of storage of the smartphones, we proposed to develop an SBA that can seamlessly process the image data on the cloud instead of local hardware of the smartphone. Therefore, we designed a study to build a machine learning model of melanoma diagnosis to measure the time taken in preprocessing, segmentation, feature extraction, and classification on the cloud and compared the results with the processing time on the smartphone's local machine. The results showed there is a significant difference of p value <0.001 on the average processing time taken on both environments. As the processing on the cloud is more efficient. The findings of the proposed research will be helpful for the developers to decide the processing platform while developing smartphone applications for automatic melanoma diagnosis.

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