scholarly journals Melanoma Skin Cancer Detection with Machine Learning

2020 ◽  
Vol 8 (10) ◽  
pp. 211-216
Author(s):  
Vishala B M
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Skin Cancer Detection ◽  
Melanoma Skin

scholarly journals Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing

2021 ◽  
Vol 2 (2) ◽  
pp. 100-106
Author(s):  
Fina Royana ◽  
Puput Yuniar Maulida ◽  
Rully Nurul Hasanah ◽  
Sondari Setia Rahayu ◽  
Rasim Rasim
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Future Health ◽  
Skin Cancers ◽  
Threatening Condition ◽  
Life Threatening ◽  
Management Techniques ◽  
Skin Cancer Detection ◽  
Melanoma Skin

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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