Breast Cancer Diagnosis: Integrating Constructive Deep Neural Network

Author(s)	Ms. Chauhan Janhvi Arvindbhai, Mr. Modi Dhaval Maheshkumar
Country	India
Abstract	The Oncotype DX (ODX) breast cancer assay is the most widely used Gene Expression Profiling (GEP) test globally. It plays a significant role in guiding decisions regarding Adjuvant Chemotherapy (ACT). Despite the availability of several standard approaches for this purpose, their accuracy has yet to reach optimal levels. This paper focuses on Breast Cancer Computer-Aided Diagnosis (BC-CAD) using a Deep Constructive Neural Network to predict the Recurrence Score (RS) provided by the ODX assay. The proposed ConstDeepNet algorithm was evaluated by developing two types of classifiers: the first uses a "one-against-all" architecture, building a separate Deep Neural Network for each class, while the second employs a single DNN to classify all three classes. A separate network is constructed for each class in the first architecture, while the second architecture utilizes a single deep neural network to classify all three classes. The proposed BC-CAD algorithm was evaluated on a real-world dataset and demonstrated strong performance. The dataset consists of 92 cases of luminal B mammary carcinoma with available Oncotype DX test results collected between 2012 and 2017 from the Georges Francois Leclerc Cancer Centre.
Keywords	deep learning, neural networks, breast cancer, recurrence score, oncotype DX
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2021
Published On	2021-01-08

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International Journal For Multidisciplinary Research