Melanoma Skin Cancer Detection Using Image Processing and Machine Learning Techniques

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.

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Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing

Jurnal Litbang Edusaintech ◽

10.51402/jle.v2i2.44 ◽

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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A Breast Cancer Detection Using Image Processing and Machine Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5906.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 5250-5256

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Image Processing ◽

Early Detection ◽

Cancer Detection ◽

Breast Cancer Detection ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Image Processing Technique ◽

Learning Techniques

Routine breast cancer screening allows the disease to be diagnosed and treated prior to it causing noticeable symptoms. During the diagnosis process there are chances of wrong detection hence a less human interfaced system has to be developed, hence the goal of breast cancer detection using machine learning techniques is used to find it before it spreads to the larger extent. Screening refers to tests and exams used to find a disease in people who don’t have any symptom. Early detection means finding and diagnosing a disease earlier than waiting for symptoms to start causing the effect on the neighboring cells. The breast cancer is the second most death causing cancer in humans, one in every ten women are affected by the breast cancer. Breast cancer is not only affecting the women. Men are also prone to get affected by the breast cancer but in smaller rates because of the absence of milk ducts and other lobules related to women. Early detection of the breast cancer helps in reducing the death rates if treated earlier and by proper diagnosis. In this paper the discussion of the various image processing technique done on the image and the CNN, SVM algorithm implementation on dataset images for the classification of malignant and non malignant cells are used and various tests were performed using different other machine learning algorithms and there level of accuracy and difference of various parameters are discussed for image processing MATLAB coding is used.

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