Journal ArticleDOI
Comparative assessment of CNN architectures for classification of breast FNAC images.
TLDR
A comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.Abstract:
Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.read more
Citations
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Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology
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Distracted driver detection by combining in-vehicle and image data using deep learning
TL;DR: This work proposes to integrate sensor data into the vision-based distracted driver detection model to improve the generalization ability of the system and shows that integrating sensor data to image-based driver detection significantly increases the overall performance with both of the fusion techniques.
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Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging
Hasnae Zerouaoui,Ali Idri +1 more
TL;DR: In this paper, a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with breast cancer imaging is conducted.
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Deep hybrid architectures for binary classification of medical breast cancer images
TL;DR: In this paper , the authors developed and evaluated twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, INception ReseNet V2, MobileNet V1, ResNet 50, VGG16, and VGG19) for binary classification of breast pathological images over the BreakHis and FNAC datasets.
Journal ArticleDOI
Deep hybrid architectures for binary classification of medical breast cancer images
Hasnae Zerouaoui,Ali Idri +1 more
TL;DR: In this article, the authors developed and evaluated twenty-eight hybrid architectures combining seven deep learning techniques for feature extraction (DenseNet 201, SVM, DT, and KNN) for binary classification of breast pathological images over the BreakHis and FNAC datasets.
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