Analysis and Implementation of Skin and Breast Cancer Prediction Using Deep Learning
Muskan Razdan, Nikhil Saini, Naman Kaushik, Mahesha AM
Breast Cancer, Skin Cancer, Deep Learning, Convolutional Neural Network, ANN
In recent years, human infections have expanded unexpectedly everywhere. And one of those is Breast Cancer, Breast Cancer has expanded at a disturbing rate in the previous decade and this pattern of increment would keep on developing. Presently, there is a requirement for proficient message investigation and element extraction instruments to help with grouping, sharing, and recovering data on human illnesses overall and Breast Cancer specifically. Skin Cancer, the most well-known human danger, is essentially analyzed outwardly, starting with an underlying clinical screening and followed possibly by dermoscopic investigation, a biopsy and histopathological assessment. Mechanized arrangement of skin sores utilizing pictures is a difficult undertaking attributable to the fine-grained changeability in the presence of skin sores. Cancer Diagnosis Assistance Tool is designed for the diagnosis of certain types of cancer which will be an outcome of deep learning, data analytics and image processing. This Project will work as an assistance tool for the doctors and it will increase the accuracy of the diagnosis. Profound learning with Convolutional Neural Networks has arisen as one of the most impressive AI apparatuses in Image order, outperforming the exactness of practically any remaining conventional arrangement techniques and surprisingly human capacity. The convolutional cycle can work on a picture containing a huge number of pixels to a bunch of little component maps, subsequently lessening the element of info information while holding the main differential highlights. Therefore in our research, CNN is used to classify the images. Our research is based on the images and CNN is the most popular technique to classify the images. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed framework, in this way, further develops accuracy by 9% above outcomes from AI (ML) calculations.
Article Details
Unique Paper ID: 154903

Publication Volume & Issue: Volume 8, Issue 12

Page(s): 685 - 692
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