Detection and Staging of Invasive Ductal Carcinoma Using Deep Learning and CNN

Breast Cancer occurs when cells in the breast grow and multiply in an uncontrolled manner. It can be localised or spread to and invade other parts of the body. Invasive Ductal Carcinoma is the most frequent sub-type of Breast Cancer documenting for over 75% of all Breast Cancer cases. Detection and...

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Bibliographic Details
Published in2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 7
Main Authors Sudhanva, Shreyas, S, Shashank C, N, Samanth, N, Chaitra
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2023
Subjects
Artificial Intelligence
Breast cancer
CNN
Computational modeling
Convolution
Deep learning
Image processing
Inception
Invasive Ductal Carcinoma
MobileNet
Prediction algorithms
Predictive models
ResNet
SVM
TensorFlow
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Summary:Breast Cancer occurs when cells in the breast grow and multiply in an uncontrolled manner. It can be localised or spread to and invade other parts of the body. Invasive Ductal Carcinoma is the most frequent sub-type of Breast Cancer documenting for over 75% of all Breast Cancer cases. Detection and diagnosis of IDC involves a combination of tests and procedures. This process can be challenging and time consuming. Usage of AI can greatly aid this process by reducing false positives and as an implication, reduce diagnosis times. Early detection and diagnosis can improve survival numbers significantly. This paper explores the prediction of malignancy, detection of IDC, predicts its appropriate stage and compares the performance of several algorithms with varying accuracies with the highest one yielding 97.32%
ISSN:2473-7674
DOI:10.1109/ICCCNT56998.2023.10308100