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Local Binary Pattern (LBP) and Transfer Learning Based Approach to Classify Lung and Colon Cancer

SN computer Science 2024年第6期5卷 783页

作者： Singh, Onkar Singh, Koushlendra Kumar Department of Computer Science and Engineering Manipal University Jaipur Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur

Several genetic disorders and other metabolic abnormalities work together to generate the lethal disease known as cancer. Today’s most contributing factors to mortality and disability in patients are lung and colon cancer. A World Health Organization (WHO) 2020 report listed cancer as one of the leading causes of mortality globally. About 2.735 million of these fatalities were caused by lung and colon cancer combined. A critical component of the patient’s treatment is the diagnosis of lung cancer by histopathology. Therefore, one of the leading research priorities, mostly in the domain of biomedical health information systems, is the identification and categorization of lung and colon cancer. The present article encompasses the Local Binary Pattern (LBP) and transfer learning-based approaches to classify lung as well as colon cancer. LBP has been used for extracting features, and transfer learning has been used for the classification of lung and colon cancer. Histopathological (LC25000) lung and colon datasets are used to validate the proposed methodology. The results of the proposed method have also been compared with different existing methods and reported in the article. Our proposed method has an average accuracy of 99.00% and a F1 score of 99.2%, whereas precision and recall have 99.4% for lung and colon cancer detection. The results of the investigation demonstrate that our suggested technique greatly outperforms current models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Histopathology images Local binary pattern (LBP) Lung cancer Transfer learning

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An Ensemble Technique for Classification of Oral Cancer by Using Histopathological Imaging 3rd

An Ensemble Technique for Classification of Oral Cancer by...

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3rd International Conference on machine vision and Augmented intelligence, MAI 2023

作者： Saikia, Trishna Dhamaniya, Ashutosh Gupta, Puneet Singh, Koushlendra Kumar Department of Computer Science and Engineering IIT Indore Indore India Machine Vision and Intelligence Lab NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789819743582

Oral cancer primarily affects the oral chamber within the head and neck area, and underscores the critical need for effective classification to initiate timely treatment. Deep learning (DL)-based computer-aided diagnostic (CAD) systems have demonstrated notable success in various applications, offering accurate and prompt diagnosis of oral squamous cell carcinomas (OSCC). One of the challenges in biomedical image classification is the acquisition of a sufficiently large training dataset. Transfer learning presents an efficient solution by extracting general features from natural image datasets and adapting them to new image datasets. In this study, we focus on classifying OSCC histopathology images to develop a productive DL-based CAD solution. To this end, we employ an average weighted ensemble technique, harnessing the strengths of deep learning-based models. To address the limitation of a small dataset, we fine-tune pre-trained deep CNN models, specifically VGG-16, VGG-19, MobileNet-V2, and Inception-V3, within our proposed method. Additionally, we conduct a comprehensive comparative analysis of these models, considering classification accuracy, precision, recall, and F-score as metrics. Our experimental findings reveal that VGG-19 consistently delivers substantially superior performance compared to the other fine-tuned deep CNN models. However, our proposed weighted ensemble technique outperforms all these deep CNN models, particularly when employing the RMSProp optimizer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble Histopathological imaging Inception-V3 MobileNet-V2 OSCC Transfer learning VGG-16 VGG-19

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computer-Aided Leaf Disease Classification Based on Logistic Regression and Transfer Learning Approach

Computer-Aided Leaf Disease Classification Based on Logistic...

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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Saikia, Trishna Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur India

ISBN: (纸本)9789819901883

Over 60% of the total Indian population depends on agriculture. It is one of the key fields of Indian Economy which comes up with 17%–18% of total GDP. Therefore, it is very important to detect and classify plant leaf diseases properly in agricultural field. Sometimes, it is difficult to detect and identify the disease correctly by naked eye. So, a computer-aided system is very much necessary to identify the disease accurately. Moreover, it is also helpful in large farms of crops as it reduces great work of monitoring each plant individually. In this research study, we have implemented three different techniques, among those one is Logistic Regression (LR) and another two techniques are the combination of transfer learning method with Support Vector machine (SVM). For all the techniques, we have used Adam optimizer. For our experiment, we have utilized a subset of PlantVillage datasets. We have compared all the methods and listed in a table. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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2D Ear Recognition Using Data Augmentation and Deep CNN

2D Ear Recognition Using Data Augmentation and Deep CNN

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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Mehta, Ravishankar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur India

ISBN: (纸本)9789819901883

Ear recognition is one of the challenging tasks in the era of computer vision. In this work, we have highlighted the role of augmentation technique on convolutional neural networks (CNNs) for the purpose of improving the recognition rate. Here, we developed a simple CNN-based model for feature extraction and classification task using ear image for person identification. Over the last few years, many researchers have worked on different techniques to improve the recognition accuracy for person identification using ear images. We evaluated our method on unconstrained and unconstrained dataset. For this purpose, we have used AWE and AMI ear datasets for the purpose of ear recognition. The results show that the application of augmentation technique improves the accuracy of the model. In contrast to other method without augmentation, training a model with augmentation of data increases the accuracy from 52 to 82%. Hence, we can conclude that the data augmentation can be applied for the recognition task using ear images when there is lack of sufficient data in the dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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A Comparative Analysis of 2D Ear Recognition for Constrained and Unconstrained Dataset Using Deep Learning Approach

A Comparative Analysis of 2D Ear Recognition for Constrained...

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International Conference on machine vision and Augmented intelligence, MAI 2022

ISBN: (纸本)9789819901883

In this paper, we have made a comparative analysis of ear recognition for constrained and unconstrained dataset. We have also analyzed the effect of image quality like illumination, occlusion, brightness, and pose variation in our work. We have addressed the problem of limited dataset for ear recognition task using deep learning-based approach. We aim at building a deep convolutional neural network model that does the task of classification for ear recognition task. Our model considers both constrained and unconstrained dataset of ear image for analysis purpose. In this paper, we have explored the benefits of deep learning and its different approached to enhance the performance ear based biometric systems where sample images are captured in constrained and unconstrained condition. This paper mainly focuses on creation of deep learning model and compared its performance on three different datasets (AWE, AMI, and IITD-II) of ear images by varying the network architecture. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Classification of Lung Nodule from CT and PET/CT Images Using Artificial Neural Network

Classification of Lung Nodule from CT and PET/CT Images Usin...

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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled "A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis." The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Pulmonary Lung Cancer Classification Using Deep Neural Networks

Pulmonary Lung Cancer Classification Using Deep Neural Netwo...

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International Conference on machine vision and Augmented intelligence, MAI 2022

作者： Goswami, Jagriti Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

Lung cancer is the leading cause of cancer-related deaths globally. computer-assisted detection (CAD) systems have previously been used for various disease diagnosis and hence can serve as an efficient tool for lung cancer diagnosis. In this paper, we study the problem of lung cancer classification using chest computed tomography (CT) scans and positron emission tomography–computed tomography (PET-CT). A subset of publicly available Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) is used to train four different deep learning models using transfer learning for classifying three different types of lung cancer: Adenocarcinoma, Small Cell Carcinoma and Squamous Cell Carcinoma, by passing raw nodule patches to the network. The models are evaluated on metrics such as accuracy, precision, recall, F1-score and Cohen’s Kappa score. ROC curves and confusion matrices are also presented to provide a graphical representation of the models’ performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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SN computer Science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Akbari, Akbar Sheikh Kumar, Arvind Reddy, B Ramachandra Singh, Koushlendra Kumar Takei, Masahiro Leeds Beckett University United Kingdom National Institute of Technology Machine Vision and Intelligence Lab Jharkhand Jamshedpur India Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur India Chiba University Department of Mechanical Engineering Chiba Japan

ISBN: (纸本)9798350378214

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets. © 2024 IEEE.

关键词： Lung cancer

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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

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SN computer Science 2022年第4期3卷 1-10页

作者： Saikia, Trishna Kumar, Ranjeet Kumar, Dilip Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur 831014 India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Convolution neural network Random forest Support vector machine

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