Mitosis detection using convolutional neural network based features

Albayrak, Abdülkadir; Bilgin, Gökhan

doi:10.1109/cinti.2016.7846429

Cited by 45 publications

(22 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this review, fifteen studies used Histopath model with CNN for classification and detection of different types of cancers as provided in Table 9. Six of these studies provided the source of data [49,50,52,54,85] while nine studies did not publish the source of data [90,91,92,93,95,101,102,104]. Two research studies used mammographs for detection along with CNN and published data source [51,53].…”

Section: Discussionmentioning

confidence: 99%

“…Some of the related papers are briefly discussed here in this section. Albayrak et al [52] designed a method based on deep learning for the extraction of features applied on histopathological images of the breast, in particular, focused on the detection of mitosis. The proposed model extracted the features from CNN which were fed to support vector machine for its training and the mitosis of the breast was detected.…”

Section: Models and Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir

Elahi

Ayub

et al. 2019

Cancers

304

182

View full text Add to dashboard Cite

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Models and Algorithmsmentioning

confidence: 99%

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir

Elahi

Ayub

et al. 2019

Cancers

304

182

View full text Add to dashboard Cite

show abstract

“…Results were very good as compared to other image diagnosis techniques. In [11], Geert Litjens et al have presented Prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes using deep learning and concluded that deep learning improves the accuracy of prostate cancer diagnosis and breast cancer staging. Authors employed CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Gupta¹,

Kaur²,

Manhas³

2019

J Multimed Inf Syst

View full text Add to dashboard Cite

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

show abstract

“…Although the list of methods used for objects search is not complete and there are other methods for finding objects [20,21], however, discussed methods allow to determine the basic models of the multi-object recognition:…”

Section: Comparison Of Multi-object Recognition Modelsmentioning

confidence: 99%

Comparison of Multi-Object Recognition Models

Parubochyi¹,

Shuvar²

2018

ELIT-2018

View full text Add to dashboard Cite

The multi-object recognition on the single image is one of the most difficult issues of the object recognition. However, this particular model of the object recognition is best suited to the requirements of a large number of application systems. The paper analyzes the main aspects of the multi-object recognition, examines the problems of the multi-object recognition and the ways of their solution. Comparison of the basic approaches to the multi-object recognition is realized, and the possibility of adapting these approaches to different types of recognition systems is analyzed.

show abstract

Mitosis detection using convolutional neural network based features

Cited by 45 publications

References 15 publications

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Comparison of Multi-Object Recognition Models

Contact Info

Product

Resources

About