A Novel Approach for IDC and ILC Kind of Breast Cancer Prediction
Keywords:
Breast Cancer, Deep Convolutional Neural Network, Deep Learning, Machine Learning, Support Vector Machine.Abstract
Breast cancer is one of the most deadly illnesses that affect women. The remarkable survival rates of this type of diseases are a result of early identification. Various machine learning techniques were employed to predict breast cancer early on. MRI imaging is a widely used early detection method. This framework uses a few machine learning methods to introduce automatic prediction of the type of breast cancer. Contrast Limited Adaptive Histogram Equalization(CLAHE) technique is used to reduce the noise , the DCNN model which contained VGG16, ResNet 50, and DenseNet201 modules used to extract features from breast mammogram images. This framework uses a few machine learning methods to introduce automatic prediction of the type of breast cancer. This research also compares another5 ML techniques with the proposed model such as Logistic Regression, Navie Bayes classifier, KNN, AdaBoosting and random forest techniques. The WDBC dataset, which is available for public access and comprises 1800 pictures and the data from 450 breast cancer patients who had digital mammography between 2018 and 2022, was used to assess this model. Accuracy and precision were used to evaluate the proposed model's performance. The simulation results show how successful the suggested model is due to its high accuracy and low computational needs.
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