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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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A hybrid feature selection method for text classification using a feature-correlation-based genetic algorithm

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Soft Computing 2024年第23期28卷 13567-13593页

作者： Farek, Lazhar Benaidja, Amira Computer Science Department University of Guelma Guelma Algeria Computer Science Department University of Setif 1 Setif Algeria Laboratory of Vision and Artificial Intelligence - LAVIA Larbi Tebessi University Tebessa Algeria

This paper introduces a new hybrid method to address the issue of redundant and irrelevant features selected by filter-based methods for text classification. The method utilizes an enhanced genetic algorithm called "Feature Correlation-based Genetic Algorithm" (FC-GA). Initially, a feature subset with the highest classification accuracy is selected by a filter-based method, which will be then used by the FC-GA to generate potential solutions by considering the correlation between features that have similar classification weights and avoiding useless random solutions. The encoding process involves assigning a value of 0 to features that provide a high degree of correlation with other features having almost the same classification information beyond a specified context, while features that are lowly correlated retain their initial code of 1. Through iterative optimization using crossover and mutation operators, the algorithm should remove redundant features that provide strong correlations and high redundancy, which could lead to improved classification performance at a lower computation cost. The aim of this study is to improve the efficiency of filter-based methods, incorporate feature correlation information into genetic algorithms, and utilize pre-optimized feature subsets to efficiently identify optimal solutions. To evaluate the effectiveness of the proposed method, SVM and NB classifiers are employed on six public datasets and compared to five well-known and effective filter-based methods. The results indicate that a significant portion (about 50%) of the features selected by reference filter-based methods are redundant. Eliminating those redundant features leads to a significant improvement in classification performance as measured by the micro-f1 measure. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Genetic algorithms

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A hybrid ensemble framework for integrating environmental parameters for improved crop yield prediction and sustainable agricultural decision-making in Iraq

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Neural Computing and Applications 2025年 1-23页

作者： Albaaji, Ghassan Faisal Chandra S S, Vinod Machine Intelligence Research Laboratory Department of Computer Science University of Kerala Thiruvananthapuram695581 India Directorate of Agriculture Wasit Governorate Wasit52001 Iraq

This study introduces the Hybrid Recurrent Ensemble Depth (HyRED) framework to enhance crop yield prediction in Iraq by integrating diverse environmental and agricultural parameters. The analysis utilises the Valued Agricultural Grounds (VAG) dataset, a novel resource developed exclusively for this study, comprising data from 1034 agricultural plots in Wasit Governorate, Iraq. This unique dataset combines soil properties, agricultural practices, meteorological data, geographic information, and remote sensing indicators such as vegetation health and biomass indices. By using this comprehensive dataset, HyRED employs an advanced hierarchical ensemble structure that integrates memory-based and pattern-recognition modules to capture both short-term temporal patterns and long-term dependencies. This approach achieves high predictive accuracy (MSE = 0.432) while reducing overfitting and enhancing generalisability. By using this novel dataset, remote sensing indicators, and advanced predictive tools, this study offers actionable recommendations for improving agricultural productivity and food security in Iraq, setting a new benchmark for sustainable agriculture research. Notable insights include the identification of optimal fertiliser rates: 60 kg / IQ Dunum for diammonium phosphate (DAP) and 100 kg / IQ Dunum for nitrogen, which increased productivity by up to 25% compared to conventional practices. Positive correlations with nitrogen (0.8985) and DAP (0.7853) underscore their importance, while negative correlations, such as soil electrical conductivity (− 0.9220) and extreme temperatures (− 0.5967), highlight environmental challenges. Integrating remote sensing indicators revealed critical growth stages and stress patterns, providing actionable insights to optimise crop management. This study sets a benchmark for sustainable agriculture by combining innovative predictive tools with a novel dataset to improve productivity and food security in Iraq. © The Author(s), under

关键词： Agro computer Decision-making Hybrid ensemble model Informatics agriculture Yield prediction

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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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Dual Encoder-Decoder Shifted Window-Based Transformer Network for Polyp Segmentation with Self-Learning Approach

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第7期5卷 3456-3469页

作者： Lijin, P. Ullah, Mohib Vats, Anuja Cheikh, Faouzi Alaya Kumar, Santhosh Nair, Madhu S. Cochin University of Science and Technology Artificial Intelligence & Computer Vision Laboratory Department of Computer Science Kerala Kochi682022 India Norwegian University of Science and Technology Gjovik2815 Norway Norwegian University of Science and Technology Norwegian Colour and Visual Computing Laboratory Gjovik2815 Norway

According to WHO reports, cancer is the leading cause of death worldwide. The second most prevalent cause of cancer-related death in both men and women is colorectal cancer (CRC). One potential approach for reducing the severity of colon cancer is to utilize automatic segmentation and detection of colorectal polyps in colonoscopy videos. This technology can assist endoscopists in quickly identifying colorectal disease, leading to earlier intervention and better patient Quality of Life (QoL). In this article, we propose a self-supervised transformer based dual encoder-decoder architecture named P-SwinNet for polyps segmentation in colonoscopy images. The P-SwinNet adapts the dual encoder-decoder type of model to enhance the feature maps by sharing multiscale information from the encoder to the decoder. The proposed model uses multiple dilated convolutions to enlarge the field of view to gather more information without increasing the computational cost and the loss of spatial information. We also leverage a large-scale unlabeled dataset for training our model using the self-learning strategy of Barlow twins. Additionally, to capture the long-range dependencies in the data, we used a shift window-based approach that computes global attention. We extensively evaluate our model against state-of-the-art algorithms. The quantitative results show that the proposed P-SwinNet achieves a mean dice score of 0.87 and a mean Intersection over Union (IoU) of 0.82 on five datasets used in our study. This performance demonstrates a substantial advancement over existing similar works, highlighting the advantage and novelty of our proposed approach in the field of medical image segmentation. © 2020 IEEE.

关键词： Convolution

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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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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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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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