scholarly journals Melanoma Skin Cancer Detection using Image Processing and Machine Learning

2019 ◽  
Vol Volume-3 (Issue-4) ◽  
pp. 780-784 ◽  
Author(s):  
Vijayalakshmi M M ◽  

Skin cancer growth is viewed as one of the most Hazardous type of the Cancers found in Humans. Nowadays skin cancer is found in different kinds for example Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the generally flighty. The detection of Melanoma disease in beginning period can be helpful for cure it. Computer vision can play big role in Portrayal Analysis also it has been examined by many existing frameworks. In this paper, we present a Computer helped strategy for the recognition of Melanoma Skin Cancer utilizing Image Processing instruments. The contribution to the framework is the skin lesion picture and after that by applying novel picture preparing strategies, it investigates it to finish up about the nearness of skin malignancy. The Lesion Image investigation instruments checks for the different Melanoma parameters Like Asymmetry, Border, Color, Diameter,(ABCD) and so on by surface, size and shape examination for picture division and highlight stages. The extricated highlight parameters are utilized to characterize the picture as Normal skin and Melanoma cancer growth injury.


2021 ◽  
Vol 2 (2) ◽  
pp. 100-106
Author(s):  
Fina Royana ◽  
Puput Yuniar Maulida ◽  
Rully Nurul Hasanah ◽  
Sondari Setia Rahayu ◽  
Rasim Rasim

Currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year (WHO, 2017). Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. Besides, consultations with dermatologists have better prognosis (Avilés-Izquierdo et. al., 2016). Due to that, we built an early skin cancer detection application with dermatologist consultation. Our application helps to diagnose skin cancer before it grows into a life-threatening condition and is crucial to preserving lifestyle, future health, and aesthetics. Besides, thanks to online doctor consultations we have, however, getting diagnosed, prescribed and treated for your issues without spending time travelling to and from the doctors and waiting in queues can be just as effective. We used three management techniques such as machine learning to create data pipelines, build a model, and convert the model to TensorFlow lite with post-training quantization. Android to deploy the TensorFlow lite model and create the application. The application has a real-time connection using firebase. Moreover, cloud to create a simple database for doctor and diagnosis services on firebase.


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