scholarly journals Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural Network

2021 ◽  
Author(s):  
Radwan Al.Shawesh ◽  
Yi Xiang Chen
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Validation Dataset ◽  
Data Sets ◽  
Classification Methods ◽  
Tumor Diseases ◽  
Histopathological Images ◽  
Death Causes ◽  
Better Than

AbstractColorectal cancer (CRC) also known as bowl cancer is one of the leading death causes worldwide. Early diagnosis has become vital for a successful treatment. Now days with the new advancements in Convolutional Neural networks (CNNs) it’s possible to classify different images of CRC into different classes. Today It is crucial for physician to take advantage of the new advancement’s in deep learning, since classification methods are becoming more and more accurate and efficient. In this study, we introduce a method to improve the classification accuracy from previous studies that used the National Center for Tumor diseases (NCT) data sets. We adapt the ResNet-50 model in our experiment to classify the CRC histopathological images. Furthermore, we utilize transfer learning and fine-tunning techniques to improve the accuracy. Our Experiment results show that ResNet_50 network is the best CNN architecture so far for classifying CRC histopathological images on the NCT Biobank open source dataset. In addition to that using transfer learning allow us to obtain 97.7% accuracy on the validation dataset, which is better than all previous results we found in literature.

Colorectal cancer characteristics and outcomes in patients less than age 50: A comparison of an urban university hospital with the NCI SEER database.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3621-3621 ◽  
Author(s):  
Edith P. Mitchell ◽  
Allan Topham ◽  
Pramila R. Anne ◽  
Scott Goldstein ◽  
Gerald Isenberg ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Older Patients ◽  
University Hospital ◽  
Data Sets ◽  
Seer Database ◽  
Pathological Features ◽  
Younger Patients ◽  
The Third ◽  
Colorectal Cancer Patients ◽  
Better Than

3621 Background: Cancer of the colon and rectum is the third most commonly occurring cancer, as well as the third leading cause of cancer deaths in American men and women. Colorectal cancer in younger patients is believed to have worse pathological features and prognosis than in older patients. The objective of this study was to assess pathological features and outcomes of CRC in patients less than age 50 using an institutional sample and comparing to the Surveillance, Epidemiology and End Results (SEER) database. Methods: Included in the study were a total of 4595 cases from the Tumor Registry at Thomas Jefferson University Hospital (TJUH) over a twenty year period from 1988 through 2007 and 290,338 cases from the Surveillance, Epidemiology and End Results (SEER) database from 1988 through 2004. Patients less than age 50 were compared to those age 50 and older. Results: Patients under age 50 with CRC presented with more advanced stage tumors in both data sets (<0.0001) , and had more poorly differentiated tumors than older patients (PTJUH=0.02754; PSEER<0.0001). Patients under 50 also had more mucinous/signet ring cell tumors with 12 percent to 8.1 percent in the TJUH data (p=0.002916) and 13.2 percent to 10.3 percent in the SEER data (p<0.0001), with younger males having the highest prevalence in both data sets. Younger patients had fewer proximal tumors than patients 50 and over, and a higher proportion of rectal tumors (p<0.001). Patients under age 50 were more likely to have positive nodes at all stages (PSEER <0.0001) relative to 50 and over, as well as more likely to develop peritoneal metastases (PTJUH=0.3507),, but less likely to have lung metastases PTJUH=0.05249) than older pts. Despite their poor pathologic features, patients under age 50 had better than or equal survival to those 50 and older. Conclusions: Colorectal cancer patients under age 50 presented with worse histological characteristics and metastasized much sooner, yet the younger patients had better than or equal survival to those ages 50 and older. Ongoing studies will assess differences in treatment and molecular features between younger and older colorectal cancer patients.

scholarly journals Instance Transfer Learning with Multisource Dynamic TrAdaBoost

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Qian Zhang ◽  
Haigang Li ◽  
Yong Zhang ◽  
Ming Li
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Negative Transfer ◽  
Traditional Learning ◽  
Dynamic Factor ◽  
Similar Distribution ◽  
Target Domain ◽  
Learning Tasks ◽  
Sample Data ◽  
Better Than

Since the transfer learning can employ knowledge in relative domains to help the learning tasks in current target domain, compared with the traditional learning it shows the advantages of reducing the learning cost and improving the learning efficiency. Focused on the situation that sample data from the transfer source domain and the target domain have similar distribution, an instance transfer learning method based on multisource dynamic TrAdaBoost is proposed in this paper. In this method, knowledge from multiple source domains is used well to avoid negative transfer; furthermore, the information that is conducive to target task learning is obtained to train candidate classifiers. The theoretical analysis suggests that the proposed algorithm improves the capability that weight entropy drifts from source to target instances by means of adding the dynamic factor, and the classification effectiveness is better than single source transfer. Finally, experimental results show that the proposed algorithm has higher classification accuracy.

scholarly journals Deep transfer learning for aortic root dilation identification in 3D ultrasound images

2018 ◽  
Vol 4 (1) ◽  
pp. 71-74 ◽  
Author(s):  
Jannis Hagenah ◽  
Mattias Heinrich ◽  
Floris Ernst
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Aortic Root ◽  
Classification Accuracy ◽  
3D Ultrasound ◽  
Small Data ◽  
Data Sets ◽  
Ultrasound Images ◽  
Small Data Sets ◽  
Learned Features

AbstractPre-operative planning of valve-sparing aortic root reconstruction relies on the automatic discrimination of healthy and pathologically dilated aortic roots. The basis of this classification are features extracted from 3D ultrasound images. In previously published approaches, handcrafted features showed a limited classification accuracy. However, feature learning is insufficient due to the small data sets available for this specific problem. In this work, we propose transfer learning to use deep learning on these small data sets. For this purpose, we used the convolutional layers of the pretrained deep neural network VGG16 as a feature extractor. To simplify the problem, we only took two prominent horizontal slices throgh the aortic root, the coaptation plane and the commissure plane, into account by stitching the features of both images together and training a Random Forest classifier on the resulting feature vectors. We evaluated this method on a data set of 48 images (24 healthy, 24 dilated) using 10-fold cross validation. Using the deep learned features we could reach a classification accuracy of 84 %, which clearly outperformed the handcrafted features (71 % accuracy). Even though the VGG16 network was trained on RGB photos and for different classification tasks, the learned features are still relevant for ultrasound image analysis of aortic root pathology identification. Hence, transfer learning makes deep learning possible even on very small ultrasound data sets.

scholarly journals Session to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals

2019 ◽  
Vol 15 (1) ◽  
pp. 13-27
Author(s):  
Zaineb Alhakeem ◽  
Ramzy Ali
Keyword(s):  
Independent Component Analysis ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Method ◽  
Common Spatial Pattern ◽  
Brain Signals ◽  
The Brain ◽  
Better Than

Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.

Impact of Antibody Panel Size on Classification Accuracy

2011 ◽  
Vol 45 (1) ◽  
pp. 85-90
Author(s):  
Inese Polaka ◽  
Arkady Borisov
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Size Reduction ◽  
Data Sets ◽  
Biomedical Data ◽  
Classification Methods ◽  
Panel Size ◽  
Antibody Panel

Impact of Antibody Panel Size on Classification Accuracy This paper experimentally studies the influence of antibody panel size reduction on classification results. The presented study includes four classification methods and five feature evaluators that are applied to five different biomedical data sets with large dimensionality (1200 features). The behaviour of the classifiers in these data sets is examined to reveal overall trends of dimensionality reduction impact on classification accuracy.

scholarly journals Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 29 ◽  
Author(s):  
Mohamed Loey ◽  
Mukdad Naman ◽  
Hala Zayed
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Blood Cells ◽  
Classification Accuracy ◽  
Automated Classification ◽  
Classification Models ◽  
Fatal Disease ◽  
Microscopic Images ◽  
Traditional Approaches ◽  
Better Than

Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.

scholarly journals Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Zixuan Feng ◽  
Liping Song ◽  
Xiangbin Liu ◽  
Shuai Liu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Dietary Habits ◽  
Recognition Rate ◽  
Learning Task ◽  
Colorectal Polyps ◽  
Data Sets ◽  
Fine Tune

With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

scholarly journals Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yogesh Gupta ◽  
Ghanshyam Raghuwanshi ◽  
Abdullah Ali H. Ahmadini ◽  
Utkarsh Sharma ◽  
Amit Kumar Mishra ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Case Reports ◽  
Data Sets ◽  
Learning Models ◽  
Weather Factors ◽  
Data Set ◽  
Competitive Model ◽  
The Impact ◽  
Sunlight Hours ◽  
Better Than

Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

scholarly journals Deep adversarial domain adaptation network

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142096464
Author(s):  
Lan Wu ◽  
Chongyang Li ◽  
Qiliang Chen ◽  
Binquan Li
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Classification Accuracy ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
Data Sets ◽  
Maximum Mean Discrepancy ◽  
Consistent Feature ◽  
Simulation Results ◽  
Domain Transfer

The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.

Feature-Weighted Local Support Vector Machine of Particle Swarm Optimization

2014 ◽  
Vol 668-669 ◽  
pp. 1147-1151
Author(s):  
Wen Bin Cui ◽  
Shao Min Mu ◽  
Chuan Huan Yin ◽  
Qing Bo Hao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Support Vector ◽  
Data Sets ◽  
Swarm Optimization ◽  
Local Support ◽  
Feature Weight ◽  
Better Than

Local support vector machine gives the feature same weight in classification. In fact, many datasets have some weak or irrelevant features related to the classification. Thus giving features same weight may reduce the classification accuracy of local support vector machine.This paper puts forward a new local support vector machine that the feature weight is optimized by PSO (Particle Swarm Optimization), it is tested on the international standard UCI data sets and the images of tree taxonomy data sets, the results show that the accuracy of the algorithm we proposed is better than the general local support vector machine.

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