Deep Integrated Pipeline of Segmentation Leading to Classification for Automated Detection of Breast Cancer from Breast Ultrasound Images

• URL: http://arxiv.org/abs/2110.14013v1
• Date: Tue, 26 Oct 2021 20:42:39 GMT
• Title: Deep Integrated Pipeline of Segmentation Leading to Classification for Automated Detection of Breast Cancer from Breast Ultrasound Images
• Authors: Muhammad Sakib Khan Inan, Fahim Irfan Alam, Rizwan Hasan
• Abstract summary: The proposed framework integrates ultrasonography image preprocessing with Simple Linear Iterative Clustering (SLIC) to tackle the complex artifact of Breast Ultrasonography Images. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer has become a symbol of tremendous concern in the modern world, as it is one of the major causes of cancer mortality worldwide. In this concern, many people are frequently screening for breast cancer in order to be identified early and avert mortality from the disease by receiving treatment. Breast Ultrasonography Images are frequently utilized by doctors to diagnose breast cancer at an early stage. However, the complex artifacts and heavily noised Breast Ultrasonography Images make detecting Breast Cancer a tough challenge. Furthermore, the ever-increasing number of patients being screened for Breast Cancer necessitates the use of automated Computer Aided Technology for high accuracy diagnosis at a cheap cost and in a short period of time. The current progress of Artificial Intelligence (AI) in the fields of Medical Image Analysis and Health Care is a boon to humanity. In this study, we have proposed a compact integrated automated pipelining framework which integrates ultrasonography image preprocessing with Simple Linear Iterative Clustering (SLIC) to tackle the complex artifact of Breast Ultrasonography Images complementing semantic segmentation with Modified U-Net leading to Breast Tumor classification with robust feature extraction using a transfer learning approach with pretrained VGG 16 model and densely connected neural network architecture. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer.

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