Breast Cancer Histopathology Image Classification with Deep Convolutional Neural Networks

Adeshina, Steve A.; Adedigba, Adeyinka P.; Adeniyi, Ahmed A.; Aibinu, Abiodun Musa

doi:10.1109/icecco.2018.8634690

Cited by 31 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[26]. Deep convolutional neural network (DCNN) with backpropagation, ensemble, and ReLU activation function is utilized in [27] for the intraclass classification. 91.5% of accuracy is achieved for the eight-class classification using the BreakHis dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

et al. 2023

View full text Add to dashboard Cite

Histopathology images are very distinctive, and one image may contain thousands of objects.Transferring features from natural images to histopathology images may not provide impressive outcomes. In this study, we have proposed a novel modality-specific CBAM-VGGNet model for classifying H and E stained breast histopathology images. Instead of using pre-trained models on ImageNet, we have trained VGG16 and VGG19 models on the same domain cancerous histopathology datasets which are then used as fixed feature extractors. We have added GAP layer and Convolutional block attention module (CBAM) after the first convolutional layer of convolutional blocks. CBAM is an effective module for neural networks to focus on relevant features. We have implemented the VGG16 and VGG19 in a novel way following the configuration of state-of-the-art models with our own concatenated layers. The addition of the GAP layer in VGGNet has reduced the number of parameters and hence requiring less computational power. Both models are ensembled using the averaging ensemble technique. Features are extracted from the final ensembled model then passed to the feed-forward neural network. A hybrid pre-processing technique is proposed that first use median filter and afterwards contrast limited adaptive histogram equalization (CLAHE). Median filter is utilized to remove the highly significant noise and is directly related to image quality. CLAHE improve the local contrast present in an image and boost the weak boundary edges in each image pixel. The proposed CBAM ensemble model has outperformed state-of-the-art models with an accuracy of 98.96% and 97.95% F1-score on 400X data of BreakHis dataset.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In [1] the authors discussed the breast cancer classification using histopathological images via using Deep Convolutional Neural Networks to solve the issue for eight cancers of either Benign or Malignant type. In their methodology, they applied deep convolutional neural networks and showed their effectiveness in the classification of images.…”

Section: ➢ Deep Convolutional Neural Networkmentioning

confidence: 99%

Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Aloyayri

Krzyżak

2020

Artificial Intelligence and Soft Computing

View full text Add to dashboard Cite

Early diagnosis of breast cancer is the most reliable and practical approach to managing cancer. Computer-aided detection or computeraided diagnosis is one of the software technology designed to assist doctors in detecting or diagnose cancer and reduce mortality via using the medical image analysis with less time. Recently, medical image analysis used Convolution Neural Networks to evaluate a vast number of data to detect cancer cells or image classification. In this thesis, we implemented transfer learning from pre-trained deep neural networks ResNet18, Inception-V3Net, and ShuffleNet in terms of binary classification and multiclass classification for breast cancer from histopathological images.We use transfer learning with the fine-tuned network results in much faster and less complicated training than a training network with randomly initialized weights from scratch. Our approach is applied to image-based breast cancer classification using histopathological images from public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 97.11% for ResNet 18, followed by 96.78% for ShuffleNet and 95.65% for Inception-V3Net. In terms of the multiclass classification of eight cancer classes, the average accuracies for pre-trained networks are as follows. ResNet18 achieved 94.17%, Inception-V3Net 92.76% and ShuffleNet 92.27%. iv AcknowledgementFirst and foremost, all praises to Allah for blessing, protecting, and guiding me throughout my studies. I could never have accomplished this without my faith.Second, I would like to express my sincere gratitude to my supervisor Professor Adam Krzyzak for his continuous support, immense knowledge, continuous motivation, and patience.His unique personality as a supervisor and friend is the main reason behind the success of this research. The objectives of the research would not have achieved without the professional and experienced guidance and support of my supervisor.I also extend my thanks to members of the examining committee, including Dr. Thomas Fevens and Dr. Tristan Glatard, for critically evaluating my thesis and providing me with valuable comments about my research. I would like to extend my thanks Halina Monkiewicz, the program advisor of Computer Science and Software Engineering, for her support, patience and motivation.Third, I would like to express my sincere gratitude to the financial support from the Government of Saudi Arabia under the scholarship of Saudi Electronic University, which enabled me to undertake my studies, and I was lucky to obtain this opportunity. I am forever thankful to the Saudi Cultural Bureau and the Embassy of Saudi Arabia in Canada for their continued support and motivation during my journey.A heartfelt deepest gratitude expresses to my first teachers (my dear mother and my father), my brothers, and my sisters for their unconditional love, prayers, and support. Without them, this journey would not have been possible, and to them, I dedicate this milestone.Last but never least, to my l...

show abstract

“…More recently, deep learning techniques have shown good performance in pattern recognition literature, using different types of neural networks, such as Convolutional Neural Networks (CNNs) [10,11]. These types of neural networks have been applied to discriminate between normal and abnormal red blood cells [12], cells infected with malaria [13], among other applications.…”

Section: Introductionmentioning

confidence: 99%

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

et al. 2021

View full text Add to dashboard Cite

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

show abstract

Breast Cancer Histopathology Image Classification with Deep Convolutional Neural Networks

Cited by 31 publications

References 4 publications

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Contact Info

Product

Resources

About