We have used a CNN model with Swish activation function as a layer and also another with Adaptive Piecewise Linear function for comparison. In our work, we have built a model that works predominantly on the Swish activation function and CNN. Have been used extensively in this area specifically because medical imaging and diagnosis require high precision and in most cases, early detection of a disease can prove vital, so, many physicians worldwide use these deep learning models as well for a thorough diagnosis. A CNN model trained on vast medical data can become a handy tool even for a physician with years of experience. Since early detection of this disease is a key factor technology has advanced to aid physicians in helpful diagnosis. It is been observed that early detection is key in this aspect and has been proven to have a 90% cure rate in low-risk patients. Nevertheless, the prognosis can be deemed good when melanoma is identified in the early stages. The mortality rate of patients with melanoma has drastically increased in the past few years which has begun to pose as a problem and one-fifth of the patients develop metastatic disease which ultimately leads to death. Melanoma is known to be rarer but it is all the more dangerous. There are two types of skin cancer, non-melanoma and melanoma. Skin cancer is the most common type of cancer which is known to generally occur in people with lesser melanin count. Deep learning is a multilayered network, and due to this factor deep learning models can perform tasks of classification in medical diagnosis and also clustering patients based on their symptoms and medical backgrounds. Most of the modern deep learning models are based on artificial neural networks like CNNs, although they can also incorporate propositional formulas that are included in a layer-wise deep network. Deep learning has been used in medical diagnosis and classification widely. KeywordsSwish, melanoma classifier, CNN, activation functionĭeep learning models mainly Convolutional Neural Networks (CNN) like Mask-R CNN, U-Net, VGG, have been extensively used to solve problems in computer vision technology. The dataset used Skin cancer MNIST: HAM10000. Swish activation function was compared with Adaptive Piecewise Linear unit to get a broader understanding of the role of activation function and its importance in image classification. After a thorough experiment, we have deduced that a convolutional neural network with Swish activation function performs well in detecting skin cancer and its types. We have built a deep learning model that detects melanoma and cases of skin cancer. Over the past years, we have seen Deep learning being a popularly used for medical diagnosis. The main reason for melanoma is sun exposure, which has proven to be the cause of 65% of melanoma cases. Although it can develop even in places where the skin is not been exposed to the sun. Skin Cancer Detection using CNN with Swish Activation Functionĭept: Computer Science Cambridge Institute of TechnologyĪbstract: The irregular growth of skin cells develops on a person's skin when it is has been exposed to the sun.
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