Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

In this chapter, deep learning-based approaches, namely deep feature extraction, fine-tuning of pre-trained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, are used to classify the malignant and normal breast X-ray images. For deep feature extraction, pre-trained deep CNN models such as ResNet18, ResNet50, ResNet101, VGG16, and VGG19 are used. For classification of the deep features, the support vector machines (SVM) classifier is used with various kernel functions namely linear, quadratic, cubic, and Gaussian, respectively. The aforementioned pre-trained deep CNN models are also used in fine-tuning procedure. A new CNN model is also proposed in end-to-end training fashion. The classification accuracy is used as performance measurements. The experimental works show that the deep learning has potential in detection of the breast cancer from the X-ray images. The deep features that are extracted from the ResNet50 model and SVM classifier with linear kernel function produced 94.7% accuracy score which the highest among all obtained.

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A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

Scientific Reports ◽

10.1038/s41598-021-85652-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Anabia Sohail ◽

Asifullah Khan ◽

Noorul Wahab ◽

Aneela Zameer ◽

Saranjam Khan

Keyword(s):

Breast Cancer ◽

Activity Index ◽

Tissue Level ◽

Cell Level ◽

Discrimination Ability ◽

Mitosis Detection ◽

Histopathological Images ◽

Deep Cnn ◽

Domain Transfer ◽

Multi Phase

AbstractThe mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

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Diagnostic Breast Cancer Image Data Classification using CNN

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1984 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

K. Karthik, Et. al.

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Image Recognition ◽

Image Data ◽

Computer Assisted ◽

Data Sets ◽

Computer Assisted Detection ◽

Deep Cnn ◽

Image Data Classification

Breast cancer has been dangerous form of cancer. In this report, we use a convolutional neural network to scan and separate infected cells.In this we diagnose if its benign or malignant cancer bulk using computer assisted detection(CAD). The productivity of open CAD has always been inadequate. Here, we use a deep CNN-based content detection method.We create narrower and broader images of histology patches with cell and tumour attributes. CNN constitutes unorganized data specifically for image data which has been said to be thriving in the area of image recognition .We use highly interconnected layer first cnn, in which those layers are incorporated before the first convolutional layer, since CNN does not support data sets.

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