Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Huang, Pan; Zhang, Shuailei; Li, Min; Wang, Jing; Ma, Cailing; Wang, Bowei; Lv, Xiaoyi

doi:10.1109/access.2020.2970121

Cited by 38 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It only covers a small area within a fixed radius, which makes it impossible to extract the features of the whole histopathological image perfectly. To compensate make up for this deficiency, and to meet the requirements of a constant grey level and rotation, this paper uses a circular neighbourhood to replace the traditional square neighbourhood LBP algorithm [32]. The LBP algorithm has the advantages of simple calculation, efficient recognition, good texture feature display and low computational complexity [33,34].…”

Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

“…The grey-gradient co-occurrence matrix synthesizes the grey level and gradient information existing in the image; that is, the image gradient information is added to the grey-level co-occurrence matrix so that the gradient information is mixed in the grey-level co-occurrence matrix and the image feature extraction effect is often better [32,37]. Based on the standardization of the grey-gradient co-occurrence matrix, we can calculate a series of secondary statistical characteristics.…”

Section: ) Glgcmmentioning

confidence: 99%

See 1 more Smart Citation

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.

show abstract

Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

Section: ) Glgcmmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The LASSO algorithm is shown in Equation (1). In addition, a 10-fold crossvalidation method was used to verify the stability of the LASSO algorithm, as shown in Figure 3 [43].…”

Section: As Shown Inmentioning

confidence: 99%

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Nie

Reheman

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learningsupport vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.

show abstract

“…Scientific research teams across the world use cervical WSIs to diagnose the degree of cervical cancer and the accuracy of WSI classification is relatively high when the degree of the disease varies greatly, especially when the classification accuracy of normal and cancerous images is almost 100%, but the overall classification accuracy is low. P. Huang et al [ 25 ] proposed a method for the classification of pathological cervical images based on the least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine, and they explored the classification relationship between images of different stages in a comprehensive manner; for pathological tissue images with large differences in disease degrees, the upper classification effect was good, especially for the normal and cancerous images, which reached 99.24%, but the recognition accuracy of early lesions was only 84.25%, and the average classification accuracy was not high. Wang, YH et al [ 26 ] proposed a computer-assisted diagnosis system for cervical intraepithelial carcinogenesis using ultra-large-scale cervical histology images to diagnose cervical intraepithelial neoplasia (CIN) along the vertical axis of the squamous epithelium.…”

Section: Introductionmentioning

confidence: 99%

AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features

Huang

Tan

Chen

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features.

show abstract

Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Cited by 38 publications

References 28 publications

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features

Contact Info

Product

Resources

About