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Modern deep learning Techniques for Big Medical Data processing in Cloud

IEEE ACCESS 2025年 13卷 62005-62028页

作者： Shakor, Mohammed Y. Khaleel, Mustafa Ibrahim Univ Garmian Coll Comp & Informat Technol Informat Technol Dept Kalar 46021 Iraq Univ Sulaimani Coll Sci Dept Comp Sulaymaniyah 46001 Iraq

The recent advancements in Machine learning (ML) and deep learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. Therefore, in this paper, an examination of the characteristics and challenges of big data is conducted with, highlighting the need for sophisticated analytical approaches. The paper then explores a wide spectrum of ML techniques, including unsupervised, supervised, and reinforcement learning methods, and their applications in big data contexts. This study explores recent advancement in DL architectures, while examining their effectiveness. Furthermore, we will show main challenges and limitations associated with using ML and DL in big data settings, including computational complexity, issues of data quality, model interpretability, and ethical concerns. Finally, it concludes by discussing the trends and future directions that are upcoming, including federated learning, explainable AI, and edge computing. Additionally, an experimental case study is presented to validate our approach, demonstrating its effectiveness in real-world healthcare scenarios through improved diagnostic accuracy and reduced computational time. Results have shown that the incorporation of DL techniques, such as CNN and RNN, forms the basis for large-scale analytics in medical data analytics due to their great contribution toward the development of accuracy and scalability. In specific research on disease prediction and medical image diagnosis within the healthcare industry, there was an improvement in diagnostics by up to 20%, with a reduction in computation time achieved via cloud-based architecture. These developments demonstrate the high extent of integration between DL and cloud computing in handling the challenge of bulk medical datasets and providing practical insight for both researchers and health professionals.

关键词： Big Data Medical diagnostic imaging Cloud computing Medical services Computer architecture Reviews Bioinformatics Heuristic algorithms Ethics deep learning Big data medical data DL cloud computing clustering classification CNN SVM ANN DNN

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ShortYOLO-CSP: a decisive incremental improvement for real-time vehicle detection

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JOURNAL OF real-time image processing 2023年第1期20卷 1-12页

作者： Rani, P. Esther Jamiya, S. Sri Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Elect & Commun Engn Chennai India

deep learning methods, particularly in the field of vehicle detection, have significantly increased in recent years. It plays an important role in detecting vehicles. In the case of autonomous driving and traffic control systems, accurate vehicle detection is necessary. To achieve accuracy, the network model's depth is increased, resulting in increased computational complexity and slow data processing. During severe weather, the majority of detection systems fail. Therefore, this research develops a new approach called ShortYOLO-CSP to enhance the detection of vehicles in challenging environments based on LittleYOLO-SPP. The depth of baseline network layers is re-designed, and Cross-Stage Partial (CSP) connection technique is used to reduce computational cost. The concatenation of the Spatial Pyramid Pooling (SPP) blocks improves the learning abilities of the model by combining spatial characteristics. To accelerate the training process, Complete Intersection over union (CIoU) loss function for bounding box regression was adopted. The proposed model not only recognizes tiny cars, but also improves the accuracy of detection in a variety of weather situations, according to extensive testing on the combined datasets of PASCAL VOC 2007, 2012, MS COCO 2014, and Indian Vehicle datasets. Additionally, this framework significantly outperforms the LittleYOLO-SPP by increasing mean average precision (mAP) by almost 10%. PASCAL VOC and COCO achieved state-of-the-art outcomes, with mAPs 91.32% and 63.27%, respectively. Furthermore, the suggested strategy also has a considerable impact on accuracy and cuts down on inference time.

关键词： Vehicle detection Object detection LittleYOLO-SPP YOLOv3-tiny Spatial pyramid pooling deep neural network Convolutional neural networks

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MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Rahman, Abdur Street, Jason Wooten, James Marufuzzaman, Mohammad Gude, Veera G. Buchanan, Randy Wang, Haifeng Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Mississippi State Univ Dept Sustainable Bioprod Mississippi State MS 39762 USA Mississippi State Univ Dept Agr & Biol Engn Mississippi State MS 39762 USA Purdue Univ Northwest Purdue Univ Northwest Water Inst PWI Hammond IN 46323 USA US Army Engineer Res & Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA

Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. Moisture content is a critical attribute of wood chips due to its direct relationship with the final product quality. Conventional techniques for determining moisture content, such as oven-drying, possess some drawbacks in terms of their time-consuming nature, potential sample damage, and lack of real-time feasibility. Furthermore, alternative techniques, including NIR spectroscopy, electrical capacitance, X-rays, and microwaves, have demonstrated potential;nevertheless, they are still constrained by issues related to portability, precision, and the expense of the required equipment. Hence, there is a need for a moisture content determination method that is instant, portable, non-destructive, inexpensive, and precise. This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips. A large-scale image dataset comprising 1,600 RGB images of wood chips has been collected and annotated with ground truth labels, utilizing the results of the oven-drying technique. Two high-performing neural networks, MoistNetLite and MoistNetMax, have been developed leveraging Neural Architecture Search (NAS) and hyperparameter optimization. The developed models are evaluated and compared with state-of-the-art deep learning models. Results demonstrate that MoistNetLite achieves 87% accuracy with minimal computational overhead, while MoistNetMax exhibits exceptional precision with a 91% accuracy in wood chip moisture content class prediction. With improved accuracy (9.6% improvement in accuracy by MoistNetMax compared to the best baseline model ResNet152V2) and faster prediction speed (MoistNetLite being twice as fast as MobileNet), our proposed MoistNet models hold great promise for the wood chip processing industry to be efficiently deployed on p

关键词： Wood chip Moisture content deep learning Machine vision Neural architecture search Hyperparameter optimization

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Energy-Efficient deep learning for Cloud Detection Onboard Nanosatellite

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9968-9985页

作者： Khalil, Imane Daghouri, Amina Chanoui, Mohammed Alae Guennoun, Zouhair Addaim, Adnane Mohammed V Univ Univ Ctr Res Space Technol Mohammadia Sch Engineers Rabat Morocco

deep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance. Traditional methods of image capture, onboard storage, and transmission face challenges such as bandwidth limitations and the transmission of cloud-obstructed images, highlighting the need for efficient onboard artificial intelligence. However, energy consumption remains a critical consideration for onboard processing, particularly in nanosatellites where resource constraints are significant. To address these challenges, we propose an optimized SegNet-based deep learning model implemented on the STM32H7 microcontroller for real-time cloud detection, designed to operate within the nanosatellite's strict energy budget. This work, conducted within the project of the UM5-EOSat nanosatellite mission, utilized captured images from the Gecko imager for model evaluation. The customized SegNet architecture, tailored with minimal kernels and layers, achieved an accuracy of 93.50%, effectively balancing performance and computational efficiency. Quantization further optimized energy consumption, achieving a reduction of 82.2% at 280 MHz. The quantized model demonstrated a memory footprint of 304 KB RAM and 110 KB Flash memory, with an inference time of 0.21 s and an energy consumption of 31.41 mJ, ensuring compatibility with the nanosatellite's resource constraints.

关键词： Computational modeling Accuracy Energy consumption Clouds Memory management Artificial intelligence deep learning Computational efficiency Power demand Microcontrollers Cloud detection deep learning energy consumption nanosatellite TinyML

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The JPEG Pleno learning-Based Point Cloud Coding Standard: Serving Man and Machine

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IEEE ACCESS 2025年 13卷 43289-43315页

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Inst Telecomunicacoes P-1049001 Lisbon Portugal Politecn Leiria ESTG P-2411901 Leiria Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.

关键词： Transform coding Point cloud compression Encoding Standards image coding Three-dimensional displays Geometry image color analysis Artificial intelligence Codecs JPEG Pleno standard learning-based coding man and machine point cloud coding

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Transfer learning Enabled Bearing Fault Detection Methods Based on image Representations of Single-Dimensional Signals

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INFORMATION SYSTEMS FRONTIERS 2024年第4期26卷 1345-1397页

作者： Deveci, Bilgin Umut Celtikoglu, Mert Albayrak, Ozlem Unal, Perin Kirci, Pinar TEKNOPAR Dept Res & Dev Ankara Turkiye Uludag Univ Dept Comp Engn Bursa Turkiye

Bearings are vital components in rotating machinery. Undetected bearing faults may result not only in financial loss, but also in the loss of lives. Hence, there exists an abundance of studies working on the early detection of bearing faults. The rising use of deep learning in recent years increased the number of imaging types/neural network architectures used for bearing fault classification, making it challenging to choose the most suitable 2-D imaging method and neural network. This study aims to address this challenge, by sharing the results of the training of eighteen imaging methods with four different networks using the same vibration data and training metrics. To further strengthen the results, the validation dataset size was taken as five times the training dataset size. The best results obtained is 99.89% accuracy by using Scattergram Filter Bank 1 as the image input, and ResNet-50 as the network for training. Prior to our work, Scattergram images have never been used for bearing fault classification. Ten out of 72 methods used in this work resulted in accuracies higher than 99.5%.

关键词： Rolling bearings fault determination Transfer learning Defect detection CNN deep learning GoogLeNet ResNet-50 SqueezeNet Inception-ResNet-v2 Signal processing time-frequency images Scattergram

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A Lightweight deep Convolutional Neural Network Implemented on FPGA and Android Devices for Detection of Breast Cancer Using Ultrasound images

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IEEE ACCESS 2024年 12卷 179190-179203页

作者： Vinod, Aditya Guddati, Prabhav Panda, Amit Kumar Tripathy, Rajesh Kumar Birla Inst Technol & Sci BITS Pilani Dept Elect & Elect Engn Hyderabad India

Breast cancer (BC) continues to be the primary cause of high mortality rates among women globally. Early and automated detection of this disease plays a significant role in clinical standards for better diagnosis and improved survival rates. Ultrasonography is a widely used non-invasive imaging test to diagnose BC. The traditional ultrasound-based automated diagnostic systems for detecting BC rely on cloud-based processing, which has high latency, requires constant internet connectivity, and raises the privacy of patient's ultrasound image data. This paper proposes a lightweight deep convolutional neural network (LWDCNN) implemented on edge devices (field programmable gate array (FPGA) and android devices) for real-time detection of BC using breast ultrasound (BUS) images. FPGA is used due to its parallel processing capability, low latency, and low power for the real-time processing of BUS images. Similarly, the Android device provides portability and a user-friendly system to process BUS images for automated detection of BC. The proposed LWDCNN is trained on the Google Cloud CPU-based framework to obtain the optimized model with weight and bias parameters. The compression methods (pruning and fixed-point precision-based representation of weight values) are applied to the LWDCNN model, and the inference of this model is performed on Android and PYNQ-Z2 FPGA-based edge devices to detect malignant tumor classes using BUS images. The proposed approach is evaluated using BUS images from Kaggle and breast ultrasound imaging databases. The experimental results reveal that the LWDCNN achieved average accuracy values of 94.15% on Google Cloud CPU and 93.16% on FPGA-based edge devices for detecting malignant tumors in the inference phase using hold-out validation. Similarly, the post-training quantization of LWDCNN with an integer 8-bit (INT8) case implemented on an Android device has yielded an accuracy value of 93.76% for detecting malignant tumors using BUS images. The pr

关键词： Malignant tumors Accuracy Databases Field programmable gate arrays Transfer learning Training Feature extraction Convolution Breast tissue image edge detection Ultrasound images breast cancer deep learning edge computing FPGA android

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A Novel Lightweight deep learning-Based Histopathological image Classification Model for IoMT

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NEURAL processing LETTERS 2023年第1期55卷 205-228页

作者： Datta Gupta, Koyel Sharma, deepak Kumar Ahmed, Shakib Gupta, Harsh Gupta, deepak Hsu, Ching-Hsien Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi India Netaji Subhas Univ Technol Dept Informat Technol New Delhi India Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India Foshan Univ Sch Math & Big Data Guangdong Hong Kong Macao Joint Lab Intelligent M Foshan 528000 Peoples R China Asia Univ Dept Comp Sci & Informat Engn Wufeng Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan

The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

关键词： deep learning Disease diagnosis Histopathological image classification IoMT

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Domain-specific image captioning: a comprehensive review

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INTERNATIONAL JOURNAL OF MULtimeDIA INFORMATION RETRIEVAL 2024年第2期13卷 1-27页

作者： Sharma, Himanshu Padha, Devanand Cent Univ Jammu Dept Comp Sci & Informat Technol Jammu 181124 Jammu & Kashmir India

An image caption is a sentence summarizing the semantic details of an image. It is a blended application of computer vision and natural language processing. The earlier research addressed this domain using machine learning approaches by modeling image captioning frameworks using hand-engineered feature extraction techniques. With the resurgence of deep-learning approaches, the development of improved and efficient image captioning frameworks is on the rise. image captioning is witnessing tremendous growth in various domains as medical, remote sensing, security, visual assistance, and multimodal search engines. In this survey, we comprehensively study the image captioning frameworks based on our proposed domain-specific taxonomy. We explore the benchmark datasets and metrics leveraged for training and evaluating image captioning models in various application domains. In addition, we also perform a comparative analysis of the reviewed models. Natural image captioning, medical image captioning, and remote sensing image captioning are currently among the most prominent application domains of image captioning. The efficacy of real-time image captioning is a challenging obstacle limiting its implementation in sensitive areas such as visual aid, remote security, and healthcare. Further challenges include the scarcity of rich domain-specific datasets, training complexity, evaluation difficulty, and a deficiency of cross-domain knowledge transfer techniques. Despite the significant contributions made, there is a need for additional efforts to develop steadfast and influential image captioning models.

关键词： Computer vision deep learning Medical image captioning Natural image captioning Remote sensing image captioning

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SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions

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SIGNAL processing-image COMMUNICATION 2025年 130卷

作者： Bouafia, Yassine Allili, Mohand Said Hebbache, Loucif Guezouli, Larbi Univ Batna Dept Comp Sci LaSTIC Lab Batna Algeria Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ J8X 3X7 Canada IRIS RnD Grp Inc Toronto ON Canada Higher Natl Sch Renewable Energies Environm & Sust Batna Algeria

Accurate detection of people in outdoor scenes plays an essential role in improving personal safety and security. However, existing human detection algorithms face significant challenges when visibility is reduced and human appearance is degraded, particularly in hazy weather conditions. To address this problem, we present a novel lightweight model based on the RetinaNet detection architecture. The model incorporates a lightweight backbone feature extractor, a dehazing functionality based on knowledge distillation (KD), and a multi-scale attention mechanism based on the Squeeze and Excitation (SE) principle. KD is achieved from a larger network trained on unhazed clear images, whereas attention is incorporated at low-level and high-level features of the network. Experimental results have shown remarkable performance, outperforming state-of-theart methods while running at 22 FPS. The combination of high accuracy and real-time capabilities makes our approach a promising solution for effective human detection in challenging weather conditions and suitable for real-time applications.

关键词： Human detection Hazy weather Lightweight Retinanet Knowledge distillation

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