Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis
TL;DR: Deep Learning assisted Efficient Adaboost Algorithm (DLA-EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques and the experimental results show that the high accuracy level has been compared to other existing systems.
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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.
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Citations
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning
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Deep Learning for Identifying Metastatic Breast Cancer
TL;DR: The power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses is demonstrated, by combining the deep learning system's predictions with the human pathologist's diagnoses.
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
TL;DR: A Stacked Sparse Autoencoder, an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer and out-performed nine other state of the art nuclear detection strategies.
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
Moi Hoon Yap,Gerard Pons,Joan Martí,Sergi Ganau,Melcior Sentís,Reyer Zwiggelaar,Adrian K. Davison,Robert Martí +7 more
TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning
Sana Ullah Khan,Naveed Islam,Zahoor Jan,Ikram Ud Din,Joel J. P. C. Rodrigues,Joel J. P. C. Rodrigues +5 more
TL;DR: A novel deep learning framework for the detection and classification of breast cancer in breast cytology images using the concept of transfer learning is proposed and it has been observed that the proposed framework outclass all the other deep learning architectures in terms of accuracy in detection and classified of breast tumor in cytological images.
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