As a trending medical imaging technique, Elastography and B-mode (ultrasound) are combined as a diagnostic tool to differentiate between benign and malignant breast lesions based on their stiffness and geometric properties. Image processing techniques are applied to the resulting images for feature extraction. Data preprocessing methods and Principal Component Analysis (PCA) as a dimensionality reduction technique are applied to the dataset. In this paper, supervised learning algorithm “Support Vector Machine (SVM)” is used for the classification of combined elastogram and B-mode images. Model validation is performed with K-fold cross-validation to ensure the generalization of the algorithm. Accuracy, confusion matrix, and logistic loss are then evaluated for the used algorithm. The maximum classification accuracy is 94.12% when using SVM with Radial Basis Function (RBF) kernel.
Here are few images of interface as well as it's results:
HOME PAGE:
BENIGN STAGE:
Predicted benign stage cancer result:
MALIGNANT STAGE:
Predicted malignant stage cancer result:
NORMAL STAGE:
Predicted normal stage cancer result:
CONCLUSION
This project focus on the earlier diagnosis of breast cancer, as the detection bring about the success of about 96% accuracy by the use of DNN algorithm for classification. By the use of python as a software platform, training and the computational time has been reduced to a greater extent than others. Breast Cancer, defined as a single cell heterogeneous disease which consists of various subtypes. Hence, there is urgent need to develop systems or methods that can help in early diagnosis and prognosis of cancer type. Moreover, females are more affected by breast cancer when compared to males. The age group which has maximum infection is above sixty and above fifty age groups. This is because these people have poor immune system as they get older so, the tumor cells get activated and spreads all over in the body. Finally, the prediction of breast cancer cases follows a linear pattern (i.e.) increasing year by year. This research work can be further extended by analyzing the breast cancer dataset by other statistical and machine learning techniques to discover the hidden and innovative results.
FUTURE ENHANCEMENTS
• The SVM algorithm could be implemented through hybrid model such that the accuracy of the project (application) could be enhanced further.
• Through Deep Learning, CNN (Convolutional Neural Network) can be used as it uses dimensionality reduction such that the quality of the input images is not compromised when compressed but the quantity/size of the input can be optimized.
These methodologies could be further implemented into the system application to produce more efficient results in the future.