Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Jing Zheng,Mingjie He,Shuang Wang,Denan Lin,Jipeng Fan,Zhongjun Gao

doi:10.1109/access.2020.2993536

Jing Zheng, Mingjie He + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2993536

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Abstract

Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells. Self-tests and Periodic clinical checks help to early diagnosis and thereby improve the survival chances significantly. The breast cancer classification is a medical method that provides researchers and scientists with a great challenge. Neural networks have recently become a popular tool in cancer data classification. In this paper, Deep Learning assisted Efficient Adaboost Algorithm (DLA-EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques. In addition to traditional computer vision approaches, tumor classification methods using transfers are being actively developed through the use of deep convolutional neural networks (CNNs). This study starts with examining the CNN-based transfer learning to characterize breast masses for different diagnostic, predictive tasks or prognostic or in several imaging modalities, such as Magnetic Resonance Imaging (MRI), Ultrasound (US), digital breast tomosynthesis and mammography. The deep learning framework contains several convolutional layers, LSTM, Max-pooling layers. The classification and error estimation that has been included in a fully connected layer and a softmax layer. This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques to find the most appropriate approach. The experimental results show that the high accuracy level of 97.2%, Sensitivity 98.3%, and Specificity 96.5% has been compared to other existing systems.

Highlights

CIRCUMSTANTIAL REVIEW AND SIGNIFICANCE OF DETECTING BREAST CANCER Breast cancer is a kind of malignant growth starting with breast tissue, usually in the interior lining of the Breast Lobules or Milk Ducts and metastasizing to other body parts [1]
The high precision in the image recognition of deep learning models can be accomplished in conjunction with human performance
convolutional neural networks (CNNs) requires extensive data for training Because the large dataset has less available, training and research has been conducted on the Internet from the most available data. https://wiki.cancerimagingarchive.net/ is the data set used for this analysis

Summary

Introduction

Breast cancer is a kind of malignant growth starting with breast tissue, usually in the interior lining of the Breast Lobules or Milk Ducts and metastasizing to other body parts [1]. An early breast cancer diagnosis can occur with any of the available imaging methods; it cannot be confirmed that these images are malignant alone [5]. The mammographic breast image is typically preprocessed to eliminate pectoral muscle in the diagnosis of breast cancer with a mammogram to encircle the detection process. J. Zheng et al.: DLA-EABA for Breast Cancer Detection and Early Diagnosis can, be restricted to the breast profile area by eliminating the pectoral muscle and background areas from the mammogram [8], [9]. Cancer tissues with higher pixel intensities are detected than the other breast region. Dense breasts have intensities for the same to those in cancer regions and tumor regions must be successfully identified [10], [11].

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