The project is a Transfer Learning and CNN trained model which can predict whether the patient has a suffering from Cancer or not by checking the images of the infected areas on the body. The model has been trained on a variety of images through which it predicts the required. In this project, the image file of the patient is upload into a software, which is GUI-based interface, developed with the help of Tkinter, and it consists of the model saved as a file and the software uses that to analyze the image and give the prediction which can help doctors to start with the medication way faster instead of waiting for the laboratory reports for the confirmation. So basically,
Skin cancer is an abnormal growth of skin cells. Most skin cancers are caused by exposure to ultraviolet (UV) light. When the skin is not protected, UV rays from sunlight or tanning beds can damage and alter skin's DNA that leads to the cancer. Deep learning model has been built to classify and identify the binary diagnostic group of melanocytic images obtained through dermoscopy. Based on the model, disease detection through dermal cell images has been investigated, and classifications on dermal cell images have been performed. Skin cancer is the most common form of cancer in the United States. The two most common types of skin cancer—basal cell and squamous cell carcinomas—are highly curable, but can be disfiguring and costly to treat. Melanoma, the third most common skin cancer, is more dangerous and causes the most deaths. The majority of cases of these three types of skin cancer are caused by overexposure to ultraviolet (UV) light. The most common warning sign of skin cancer is a change on the skin, typically a new mole, a new skin lesion or a change in an existing mole.
- Basal cell carcinoma may appear as a small, smooth, pearly, or waxy bump on the face, or neck, or as a flat, pink/red- or brown-coloured lesion on the trunk, arms or legs.
- Squamous cell carcinoma can appear as a firm, red nodule, or as a rough, scaly, flat lesion that may itch, bleed and become crusty. Both basal cell and squamous cell cancers mainly occur on areas of the skin frequently exposed to the sun, but can occur anywhere.
- Melanoma usually appears as a pigmented patch or bump. It may resemble a normal mole, but usually has a more irregular appearance.
Skin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. The American Cancer Society estimates over 100,000 new melanoma cases will be diagnosed in 2020. It's also expected that almost 7,000 people will die from the disease. As with other cancers, early and accurate detection—potentially aided by data science—can make treatment more effective.
Currently, dermatologists evaluate every one of a patient's moles to identify outlier lesions or “ugly ducklings” that are most likely to be melanoma. Existing AI approaches have not adequately considered this clinical frame of reference. Dermatologists could enhance their diagnostic accuracy if detection algorithms take into account “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work.
As the leading healthcare organization for informatics in medical imaging, the Society for Imaging Informatics in Medicine (SIIM)'s mission is to advance medical imaging informatics through education, research, and innovation in a multi-disciplinary community. SIIM is joined by the International Skin Imaging Collaboration (ISIC), an international effort to improve melanoma diagnosis. The ISIC Archive contains the largest publicly available collection of quality-controlled dermoscopic images of skin lesions.
Melanoma is a deadly disease, but if caught early, most melanomas can be cured with minor surgery. Image analysis tools that automate the diagnosis of melanoma will improve dermatologists' diagnostic accuracy. Better detection of melanoma has the opportunity to positively impact millions of people.