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DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression

Neural Computing and Applications 2024年第29期36卷 18363-18376页

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & amp Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

Genomic sequencing has become increasingly prevalent, generating massive amounts of data and facing a significant challenge in long-term storage and transmission. A solution that reduces the storage and transfer requirements without compromising data integrity is needed. The effectiveness of neural networks has already been endorsed in tasks like image and speech compression. Adapting them to recognize the intricate patterns in genomic sequences could help to find more redundancies and reduce storage requirements. The proposed method, called DNACoder, leverages deep learning techniques to achieve significant compression ratios while preserving the essential information in genomic data and offers a high-performance compression for genomic sequences in any data format. The results of the experiments clearly demonstrate the effectiveness of the method and its potential applications in genomic data storage. Our proposed method improves compression by 21.1% on bits per base compared to existing compressors on the benchmarked dataset. By using a deep learning prediction model that is structured as a convolutional layer followed by an attention-based long short-term memory network, we propose a novel lossless and reference-free compression approach (DNACoder), which can also be utilized as a reference-based compressor. The experimental outcome on the tested data illustrates that the advocated compression algorithm’s CNN-LSTM model makes generalizations effectively for genomic sequence data and outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

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

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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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Correction to: DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression (Neural Computing and Applications, (2024), 36, 29, (18363-18376), 10.1007/s00521-024-10130-4)

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Neural Computing and Applications 2025年 1-2页

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

In this article, the Eq. (2) and Eq. (3) were incorrectly displayed and it should have been displayed as given below (Formula presented.) (Formula presented.) where n is the total number of key-value pairs in the sequence The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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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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Towards a Feasible Hand Gesture Recognition System as Sterile Non-contact Interface in the Operating Room with 3D Convolutional Neural Network

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Informatica (Slovenia) 2022年第1期46卷 1-12页

作者： Salvador, Roy Amante A. Naval, Prospero C. Computer Vision and Machine Intelligence Group Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City Philippines

Operating surgeons are constrained when interacting with computer systems as they traditionally utilize hand-held devices such as keyboard and mouse. Studies have previously proposed and shown the use of hand gestures is an efficient, touchless way of interfacing with such systems to maintain a sterile field. In this paper, we propose a Deep computer vision-based Hand Gesture Recognition framework to facilitate the interaction. We trained a 3D Convolutional Neural Network with a very large scale dataset to classify hand gestures robustly. This network became the core component of a prototype application requiring intraoperative navigation of medical images of a patient. Usability evaluation with surgeons demonstrates the application would work and a hand gesture lexicon that is germane to Medical Image Navigation was defined. By completing one cycle of usability engineering, we prove the feasibility of using the proposed framework inside the Operating Room. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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VSMCNN-dynamic summarization of videos using salient features from multi-CNN model

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Journal of Ambient intelligence and Humanized Computing 2023年第10期14卷 14071-14080页

作者： Nair, Madhu S. Mohan, Jesna Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Nalanchira Kerala Thiruvananthapuram695015 India

A dynamic video summarization system detects key parts of the input video to generate its compact representation. The summaries can be used for efficient management of video data. This paper proposes an approach, Video summarization based on multi-CNN model (VSMCNN), that exploits major aspects of human cognition to generate meaningful summaries from videos. As the method focuses on dynamic summarization, the input video is divided into a set of shots. A multi-CNN model, which is a combination of different pre-trained models of CNN, is used for feature extraction from shots. The salient features are extracted from high dimensional feature vector using an unsupervised feature reduction technique applied in multiple subspaces to rank features in the vector. The distance measure between feature vectors is then thresholded to detect prime parts of the tested video. Experiments are performed on SumMe dataset and the results prove that our approach is successful in detecting portions of the tested video that has an essential message. The analysis shows that the method outperforms the state-of-the-art methods in the literature. Further evaluation on comparison with human-generated summaries in the ground truth proves the effectiveness of the proposed method. The paper also presents a detailed analysis to show which combination of pre-trained models of CNN is best suitable for generating dynamic summaries. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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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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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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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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