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Breast Cancer Detection Using Mammography: image processing to deep learning

IEEE ACCESS 2025年 13卷 60776-60801页

作者： Qureshi, Shahzad Ahmad Aziz-Ul-Rehman, Lal Hussain, Lal Sadiq, Touseef Shah, Syed Taimoor Hussain Mir, Adil Aslam Nadim, Muhammad Amin Williams, Darnell K. Adrian Duong, Tim Q. Chaudhary, Qurat-Ul-Ain Habib, Natasha Ahmad, Asrar Shah, Syed Adil Hussain Pakistan Inst Engn & Appl Sci PIEAS Dept Comp & Informat Sci Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Ctr Math Sci Islamabad 45650 Pakistan Macquarie Univ Dept Phys & Astron Sydney NSW 2019 Australia Pakistan Inst Engn & Appl Sci PIEAS Natl Inst Lasers & Optron Coll Agr & Biophoton Div Islamabad 45650 Pakistan Univ Azad Jammu & Kashmir Dept Comp Sci & Informat Technol King Abdullah Campus Muzaffarabad 13100 Azad Kashmir Pakistan Univ Azad Jammu & Kashmir Dept Comp Sci & Informat Technol Neelum Campus Athmuqam 13230 Azad Kashmir Pakistan Univ Agder Ctr Artificial Intelligence Res CAIR Dept Informat & Commun Technol N-4879 Grimstad Norway Politecn Torino Dept Mech & Aerosp Engn PolitoBIOMed Lab I-10129 Turin Italy Univ Telemat Pegaso Learning Sci & Digital Technol I-80143 Naples Italy Univ Foggia Learning Sci & Digital Technol I-71122 Foggia Italy Albert Einstein Coll Med Dept Radiol Bronx NY 10461 USA Pakistan Inst Engn & Appl Sci PIEAS Dept Phys & Appl Math Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Dept Med Sci Islamabad 45650 Pakistan GPI SpA Dept Res & Dev R&D I-38123 Trento Italy

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability development goals.

关键词： Breast cancer Reviews Accuracy Feature extraction STEM Open Access Radio frequency Mortality Manuals mammography microcalcification deep learning convolution neural networks machine learning machine learning

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A real-time Drone image processing System using deep learning and Big Data Technology

A Real-Time Drone Image Processing System using Deep Learnin...

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2024 RIVF International Conference on Computing and Communication Technologies, RIVF 2024

作者： Nguyen, Anh-Tuan Do, Trong-Hop University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331505073

In this research, we delve into advanced image segmentation techniques applied to drone imagery for various environmental and surveillance applications. By leveraging state-of-the-art models such as UNet, deepLabV3, Manet, and Feature Pyramid Network (FPN), our goal is to achieve high precision in segmenting complex aerial scenes. Each of these models possesses unique strengths and weaknesses;hence, we employ an ensemble technique, weighted averaging, to harness their combined capabilities for superior results. Additionally, we incorporate image augmentation techniques to simulate various weather conditions such as haze and raindrops, enhancing the robustness of our models. To manage real-time data efficiently, we implement a streaming pipeline using Apache Kafka and Apache Spark, ensuring scalable and effective processing. Our methods demonstrate significant performance improvements when trained on the original dataset and the combination of original dataset and augmented dataset compared to conventional methods. © 2024 IEEE.

关键词： image segmentation

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Advances in deep learning for real-time image and video reconstruction and processing

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JOURNAL OF real-time image processing 2020年第6期17卷 1883-1884页

作者： Shamsolmoali, Pourya Celebi, M. Emre Wang, Ruili Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Univ Cent Arkansas Dept Comp Sci Conway AR USA Massey Univ Sch Nat & Math Sci Auckland New Zealand

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image processing and Visualization Technology Application of an Improved deep learning Algorithm 2

Image Processing and Visualization Technology Application of...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Deng, Yunsheng Huang, Yinghui Chen, Zhangbao Chen, Yan Bengbu University Bengbu Anhui Huainan City China

ISBN: (纸本)9798350372892

In today's world, technology is changing our way of life and work at an alarming rate. This paper studies the performance of an improved deep learning algorithm in image processing tasks, introduces the implementation principle of algorithm design, and puts forward an improved deep learning algorithm. In the experimental method part, a group of experiments are designed to evaluate the image quality performance of the improved deep learning algorithm, which is based on three key performance indicators: Peak Signal-to-Noise Ratio (PSNR), Interactive Response time (IRT) and Structural Similarity Index Measure (SSIM). The research conclusion shows that the peak signal-to-noise ratio (PSNR) of the improved image quality-preserving deep learning algorithm is as high as 58 dB. The maximum IRT measurement of the improved algorithm is only 95 ms, which provides users with faster response speed and enables users to experience a smoother interactive experience in real-time image processing applications. © 2024 IEEE.

关键词： Contrastive learning

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Automatic Volume Control Using image processing and deep learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Theraja, Kinshuk Gupta, Sakshi Gupta, deepak Kumar Amity Institute of Information and Technology Amity University Noida India

ISBN: (纸本)9798350375237

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system. © 2024 IEEE.

关键词： image processing

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deep learning in image processing and Pattern Recognition

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ELECTRONICS 2025年第10期14卷 1942-1942页

作者： Wang, Aili Wu, Haibin Iwahori, Yuji Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Laser Spect Technol & Ap Harbin 150080 Peoples R China Chubu Univ Dept Comp Sci 1200 Matsumoto Cho Kasugai 4878501 Japan

The current field shows a trend of multi-dimensional fusion [1], the use of lightweight convolutional self-encoder and generative adversarial network in denoising, super-resolution tasks beyond the traditional methods, and multimodal fusion technology through the integration of visible/infrared/depth map data to enhance feature extraction. In future, it is necessary to build a quantum entanglement parallel denoising system, develop neural radiation field three-dimensional dynamic reconstruction technology, and integrate optoelectronic hardware design to guarantee data security [2].A self-supervised and comparative learning framework significantly reduces the dependence on labeled data [3], and the attention mechanism is combined with reinforcement learning to optimize dynamic sampling. In future, it is necessary to build a self-supervised contrast collaboration framework, develop Transformer–dynamic convolution hybrid architecture [4], and strengthen cross-scale modeling and *** Transformer dominates image classification and segmentation through the self-attention mechanism, and dynamic sparse attention improves real-time analysis capabilities [5]. In future, we need to design a multimodal synergy framework, develop a physical embedding model to integrate a priori knowledge such as light field equations, and combine it with dynamic pruning to balance performance [6].In the field of intelligent transportation, multi-sensor fusion is used to build high-precision 3D environment models [7], event cameras help to break through the traditional frame rate limitations, and federated learning is employed to optimize global traffic prediction. In future, we need to develop an impulse neural network to drive heterogeneous data alignment, construct a meta-learning cross-domain adaptive framework, and establish a privacy security sharing mechanism [8].End-to-end models are employed to realize the accurate classification of agricultural pests and diseases, whe

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A two-stage deep transfer learning model and its application for medical image processing in Traditional Chinese Medicine

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 108060-108060页

作者： Zhang, Xu Chen, Zhikui Gao, Jing Huang, Wei Li, Peng Zhang, Jianing Dalian Univ Technol Sch Software Technol Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Dalian Med Univ Dept Crit Care Med Affiliated Hosp 1 Dalian Peoples R China

People are deepening the study on model explainability as well as performance to better understand models' decisions from the human perspective. However, the lack of rare clinical diagnosis data always limits the power of the emerging data-driven deep diagnosis methods, and the traditional deep transfer learning (DTL) applicable for the case is often insufficient to learn specific features in medical image processing, leading to poor explainability. To address those challenges, a two-stage deep transfer learning model is proposed and applied to assist in the Traditional Chinese Medicine (TCM) tongue diagnosis. Especially, a two-stage transfer learning training strategy is designed to loose the data dependence of deep learning on the domain data, which is composed of the imitate stage that discovers shared basic source features and the transfer stage to relearn target patterns, with good explainability. The corresponding deep squeeze-and-excitation convolutional network is proposed to learn the clinical patterns of tongue symptoms, in which a three-layer feature pyramid network fuses the multi-scale tongue features. Extensive experiments are conducted on the real clinical dataset in terms of classification accuracy and learning efficiency. The resulting accuracy of the proposed model proves its performance advantage with the recognition time achieving real-time performance. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep transfer learning deep neural networks Medical image processing Traditional Chinese Medicine Tongue diagnosis

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TrashIntelAI- Garbage Collection Automation using deep learning and image processing

TrashIntelAI- Garbage Collection Automation using Deep Learn...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jadhav, Ranjana Mhaske, Aditya Nawale, Aditya Oswal, Mokshit Mulhar, Sumit Vishwakarma Institute of Technology Dept. of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350308167

Garbage collection in urban areas has become a major challenge due to the increase in trash production. New technologies, including the application of deep learning and image processing methods, have been created to solve this problem. In this study, we present an automated garbage collection system that recognizes and categorizes rubbish in real-time using computer vision and machine learning methods. A deep learning model classifies the garbage into several categories, and the system also contains an image processing pipeline that pre-processes incoming photos and extracts relevant data. We executed experiments on a real-world dataset to assess the performance of the suggested solution. The outcomes demonstrate that the suggested approach classifies garbage with a high degree of accuracy. The system can recognize and categorize various waste categories, including recyclables, organic garbage, and non-recyclables, which can then be collected individually for proper disposal. This is made possible by the application of deep learning and image processing algorithms. The suggested approach can potentially increase trash disposal efficiency, reduce waste sent to landfills, and promote a cleaner and healthier environment in metropolitan settings. The burden of garbage collection staff can be reduced, and the danger of harm can be reduced, with real-time monitoring and automated waste collection. © 2023 IEEE.

关键词： deep learning image processing mobile application object detection smart city waste management

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deep learning Computer Vision Algorithms for real-time UAVs On-board Camera image processing

arXiv

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arXiv 2022年

作者： Palmas, Alessandro Andronico, Pietro Nurjana Technologies srl Italy

This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. Four use cases are considered: target detection, classification and localization, road segmentation for autonomous navigation in GNSS-denied zones, human body segmentation, and human action recognition. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks. Acquisition campaigns have been carried out to collect custom datasets reflecting typical operational scenarios, where the peculiar point of view of a multi-rotor UAV is replicated. Algorithms architectures and trained models performances are reported, showing high levels of both accuracy and inference speed. Output examples and on-field videos are presented, demonstrating models operation when deployed on a GPU-powered commercial embedded device (NVIDIA Jetson Xavier) mounted on board of a custom quad-rotor, paving the way to enabling high level autonomy. Copyright © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Efficient tomato harvesting robot based on image processing and deep learning

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PRECISION AGRICULTURE 2023年第1期24卷 254-287页

作者： Miao, Zhonghua Yu, Xiaoyou Li, Nan Zhang, Zhe He, Chuangxin Li, Zhao Deng, Chunyu Sun, Teng Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Intelligent Equipment & Robot Lab Shangda St 99 Shanghai Peoples R China

Agricultural robots are rapidly becoming more advanced with the development of relevant technologies and in great demand to guarantee food supply. As such, they are slated to play an important role in precision agriculture. For tomato production, harvesting employs over 40% of the total workforce. Therefore, it is meaningful to develop a robot harvester to assist workers. The objective of this work is to understand the factors restricting the recognition accuracy using image processing and deep learning methods, and improve the performance of crop detection in agricultural complex environment. With the accurate recognition of the growing status and location of crops, temporal management of the crop and selective harvesting can be available, and issues caused by the growing shortage of agricultural labour can be alleviated. In this respect, this work integrates the classic image processing methods with the YOLOv5 (You only look once version 5) network to increase the accuracy and robustness of tomato and stem perception. As a consequence, an algorithm to estimate the degree of maturity of truss tomatoes (clusters of individual tomatoes) and an integrated method to locate stems based on the resultant experiments error of each individual method were proposed. Both indoor and real-filed tests were carried out using a robot harvester. The results proved the high accuracy of the proposed algorithms under varied illumination conditions, with an average deviation of 2 mm from the ground-truth. The robot can be guided to harvest truss tomatoes efficiently, with an average operating time of 9 s/cluster.

关键词： image processing YOLOv5 network Agriculture robot Tomato harvesting

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