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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

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Patient-centric information management in blockchain and interplanetary storage

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 85-96页

作者： Dewangan, Narendra Chandrakar, Preeti Department of Computer Science and Engineering National Institute of Technology Raipur G.E. Road Chhattisgarh Raipur492010 India

In the healthcare sector, the protection of patient information is an essential factor in terms of secrecy and privacy. This information is very useful for industrial and research purposes. Electronic medical records (EMR) hold data related to patients and their treatments. According to GDPR (general data protection regulation), patient information should be anonymous. There should not be any disclosure of patients private information during data transfer. For hiding patients private information, encryption is required. For developing countries, the treatment cost is also a matter for health services. Blockchain technology provides facilities like distributed and immutable data storage and peer-to-peer data transfer. We need cheaper blockchain-based solutions for the healthcare sector. IPFS provides a secure way to store the content of the files in a decentralized manner by using the Merkle tree. By using IPFS with blockchain, we can counter the use of fake medical records or the forge of EMR. In this paper, the interplanetary file system (IPFS) is used to store EMR in compliance with GDPR. We work on the treatment cycle of patients with a blockchain-maintained hospital and doctor. Our proposed system enhances the security and transparency of patients’ treatment information. We compared our proposed system with previously developed systems and provided analytical results in terms of security, privacy, and cost. We implemented our proposed system on a Python-customized blockchain with a customized consensus algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Electronic health record

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Evaluating Deep Learning Algorithms for MRI-Based Brain Tumor Classification

Evaluating Deep Learning Algorithms for MRI-Based Brain Tumo...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Gurpreet Chhabra, Amit Mittal, Ajay Department of Computer Science and Engineering Chandigarh India University Institute of Engineering and Technology Panjab University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350376470

The classification of brain tumors has significant importance in the realm of clinical diagnosis and the implementation of appropriate treatment strategies. The process of diagnosing a brain tumor is time-consuming and strongly dependent on the expertise and experience of the radiologist. The volume of data that necessitates processing has seen a significant surge in parallel with the growth in patient population, rendering previous protocols financially burdensome and inefficient. Numerous researchers have conducted investigations on a range of algorithms aimed at the detection and classification of brain tumors, with a focus on achieving high levels of accuracy and computational efficiency. In recent times, there has been a surge in the utilization of Deep Learning (DL) methodologies to construct automated systems that possess the ability to effectively diagnose or segment brain tumors within a reduced timeframe. Deep learning (DL) facilitates the use of a pre-existing Convolutional Neural Network (CNN) model designed for the analysis of medical pictures, with a special focus on the classification of brain tumors. The rapid advancement of Deep Learning techniques in the context of computer vision tasks has been made possible by the availability of extensive training datasets and advancements in model architecture, leading to enhanced approximations within a supervised learning framework. The growth has mostly been motivated by the need to implement these ideas. The use of deep learning methods has shown potential in enhancing the accuracy of brain tumor identification and classification via the utilization of magnetic resonance imaging (MRI). According to the findings, CNN is a superior technique that greatly boosts the accuracy by 93.4% and it is maximum as compared to other methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Industrial Anomaly Detection with Auto Encoder-Based Temporal Convolutional Networks for Motor Fault Classification

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SN computer science 2024年第8期5卷 1-12页

作者： Varalakshmi, B.D. Lingaraju, G.M. Department of Computer Science and Engineering Acharya Institute of Technology Karnataka Bangalore India Department of Information Science and Engineering MS Ramaiah Institute of Technology Karnataka Bangalore India

The creation of binary and multi-classification models with the goal of accurately detecting and categorizing motor defects is important to study. This work explores how autoencoders can be used to apply self-supervised learning in industrial settings. A generic temporal convolutional network with an autoencoder was built and trained using the milling data. The successful identification and classification of motor faults underscore the industrial anomaly detection using advanced machine learning techniques. Also, paper highlights the importance of data processing and domain expertise in achieving significant model performance improvements. The performance used a fraction of the data that was available, highlighting the need of future research to include a more extensive range of fault states and the intensities of those fault states in order to enhance the diagnostic capabilities of the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Autoencoder Deep learning Machine learning TCN (Temporal Convolution network) UC Berkeley milling dataset

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TESA: Tagging of Educational Videos Using Semantics Oriented Artificial Intelligence 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Krishnan, A. Aravind Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bangalore India

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

This paper proposes a tag recommendation model for tagging educational videos powered by semantics-oriented learning and reasoning. The framework is in accordance with the standards of the Web3.0 and applies the CNN classifier into the educational video dataset twice independently using contents and annotations. The entities of the dataset undergo RDF generation from which RDF subject and object are obtained separately. The model then subjects the result to the generation of auxiliary knowledge using Google’s KG API and Wikidata. This is included in the model to improve the number of concepts in the dataset which are distinct and diverse. The metadata generation helps in increasing the amount of auxiliary knowledge added into the dataset which is classified using the Deep Belief Networks (DNBs), a strong deep learning classifier. Semantics-oriented reasoning is, upon inference, through semantic similarity measures like the Second Order Co-occurrence PMI and the Petriatis Index in the model, inducted into the framework. Fire Hawk Optimizer (FHO) is an optimization algorithm that helps in yielding the best-in-class optimal solution set which is subjected to review and comprises the final tags for the educational videos. The framework yields the highest values of primary evaluation metrics as well as a low FDR of 0.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Secure Access Control of Cloud Data Using DNA Computing 5

Secure Access Control of Cloud Data Using DNA Computing

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Tarun Kumar, Prabhat Namasudra, Suyel National Institute of Technology Department of Computer Science and Engineering Bihar Patna India National Institute of Technology Department of Computer Science and Engineering Tripura Agartala India

ISBN: (纸本)9798350383522

With the exponential development in the usage of cloud data storage across a variety of applications, developing effective access control measures to secure sensitive user data has become critical. This research presents a unique way to use deoxyribonucleic acid (DNA) computing to improve multi-factor authentication enforcement and real-time access controls in cloud storage systems. A synthetic, engineered DNA framework is intended to reliably tie user identification characteristics to randomly produced nucleic acid credentials. After multimodal encrypted biometric authentication, the system provides these DNA access tokens. They may be validated via fast polymerase cycling to enable selective decryption and, eventually, manage allowed actions on stored cloud data based on fine-grained, attributes-based access policies. The method makes revocation happen by using programmable nucleases to quickly remove access rights from DNA structures that are waiting to be used. Rigorous cryptanalysis outperforms current access paradigms in terms of robustness to both brute force and side channel assaults. With significant developments in DNA synthesis and sequencing technologies allowing for scale deployment, bio-cryptographic access control provides a strong method for improving cloud data security via DNA computing. The system greatly increases the obstacles to unwanted data access while protecting user privacy. This study is a pioneering analysis of organizing principles in biologic data processing systems to solve key data security concerns posed by growing cloud use across commercial and public sector enterprises. © 2024 IEEE.

关键词： Access Control Cloud Data Security DNA Computing DNA Cryptography Multi-factor Authentication

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Quantum-Inspired Differential Evolution Based Efficient IoT Service Placement in Edge Networks 15

Quantum-Inspired Differential Evolution Based Efficient IoT ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bey, Marlom Kuila, Pratyay Naik, Banavath Balaji Department of Computer Science and Engineering National Institute of Technology Sikkim 737139 India Department of Computer Science and Engineering National Institute of Technology Patna800005 India

ISBN: (纸本)9798350370249

The rise in Internet of Things (IoT) devices has led to the creation of sophisticated applications that demand various resources in real time to support a wide range of IoT services. Leveraging edge computing (EC) infrastructure, these services can be effectively placed on edge nodes (ENs). However, due to the limited computational resources of ENs, it becomes challenging to manage a large number of services while maintaining the system's quality of service (QoS) and quality of experience (QoE). This paper introduces a quantum-inspired differential evolution method (QIDE-IoTSP) designed to optimize the placement of IoT services within EC networks. The primary objectives of QIDE-IoTSP are to maximize throughput, ensure optimal load balance, and minimize computation time. A quantum vector (QV) is utilized to develop a robust solution for the optimal deployment of IoT services in EC networks to achieve this. The effectiveness of each solution is evaluated using a formulated fitness function. Simulation results demonstrate that QIDE-IoTSP surpasses other metaheuristic techniques in terms of throughput, computation latency, and load balancing. © 2024 IEEE.

关键词： Quantum computers

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Optimizing Early Detection of Lung Disease Through Data-Driven Approaches 8

Optimizing Early Detection of Lung Disease Through Data-Driv...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Balapriya, S. Anandaraj, B. Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Madnapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

Lung illness is any condition that impairs the lungs' capacity to function normally;diabetes, in particular, can have a variety of effects on the lungs and increases the risk of respiratory infections such as pneumonia and TB in those who have it. According to recent studies, 384 million individuals have chronic obstructive pulmonary disease (COPD), and 3 million of them pass away each year as a result of delayed diagnosis. Early detection of any disease can lessen its severity, which will lower the death rate and number of lung disease sufferers. Used deep learning methods for lung detection diagnostics, such as improved fish bee (IFB) and chaotic bird swam optimization (CBSO), despite the prediction rate's high false rate and lack of accuracy. Introduced a technique using machine learning methods, such as the Principal Component Analysis (PCA) algorithm for extraction of features, the Decision Tree approach for sample categorization, and the Support Vector Machine (SVM) for very accurate early illness diagnosis. The results of the suggested approach for early lung tumor identification with a low incorrect rate showed an accuracy of 98.07. © 2025 IEEE.

关键词： Principal component analysis

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