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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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Hepatocellular Carcinoma Recognition from Ultrasound Images by Fusing Convolutional Neural Networks at Decision Level

Hepatocellular Carcinoma Recognition from Ultrasound Images ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Delia Mitrea Raluca Brehar Cristinel Mocan Sergiu Nedevschi Mihai Socaciu Radu Badea Department of Computer Science Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Medical Imaging I. Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania

The Hepatocellular Carcinoma (HCC) is the most often met malignant tumor of the liver. It develops from cirrhosis, after a parenchyma restructuring phase, at the end of which dysplastic nodules result that can transform into HCC. Nowadays, HCC diagnosis is usually performed through needle biopsy, also through medical imaging, but most of the methods are invasive and/or expensive. Ultrasonography is the best option for screening, while computerized techniques must be developed as well to derive subtle information. In our previous research, we experimented with conventional techniques, based on advanced texture analysis methods and traditional classifiers, as well as with deep learning techniques, aiming to perform HCC recognition with maximum accuracy. We combined the CNN architectures with each other, respectively with the conventional techniques, at classifier level. In this work, we combined existing and original CNN architectures at decision level, we assessed the corresponding performance, and we compared it with our previous results.

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Improved Depth-Aware Circuit Partitioning for Mixed Logic Synthesis

Improved Depth-Aware Circuit Partitioning for Mixed Logic Sy...

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Electronics Design automation (ISEDA), International Symposium of

作者： Chunliu Liao Yong Xiao Yun Shao Zhufei Chu Faculty of Electrical Engineering & Computer Science Ningbo University Ningbo China Giga Design Automation Co. Ltd. Shenzhen China

Mixed logic synthesis takes advantage of distinct logic representations through circuit partitioning, classification and optimization, and merging. How circuits are partitioned plays a significant role in the quality of results (QoR) of mixed logic synthesis. Currently, existing methods employ either a naive or a structurally aware approach. However, the critical path may be destructed by circuit merging due to the critical path segment being disconnected by partitions. To address this issue, we propose an improved depth aware circuit partitioning method for mixed logic synthesis. To be specific, we treat the critical path as an entity and set the partition size dynamically according to the logic depth of the input circuit. Based on the EPFL benchmark suites, XOR-Majority Graph (XMG) achieves a 19% reduction in logic depth with a 6% logic node increase over standard mixed-synthesis.

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A variable terminal set NMPC construction: application to multicopter stabilisation

A variable terminal set NMPC construction: application to mu...

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European Control Conference (ECC)

作者： Bogdan Gheorghe Florin Stoican Ionela Prodan Faculty of Automation Control and Computer Science University Politehnica of Bucharest Romania Univ. Grenoble Alpes Grenoble INP LCIS Valence France

In this paper we relax the standard NMPC (Nonlinear Model Predictive Control) through a variable terminal set construction for stabilizing a multicopter system. This contribution allows us to increase the feasible domain and/or reduce the required prediction horizon length. Furthermore, to reduce the complexity of the computations we use zonotopic sets which prove instrumental due to their efficient representation. The theoretical results are validated in simulation and experiment for the stabilisation along a trajectory of a nano-multicopter’s translational dynamics.

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An Integrated Logic Function Decomposition Flow for XOR-Majority Graphs

An Integrated Logic Function Decomposition Flow for XOR-Majo...

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Electronics Design automation (ISEDA), International Symposium of

作者： Chengyu Ma Yong Xiao Yun Shao Zhufei Chu Faculty of Electrical Engineering & Computer Science Ningbo University Ningbo China Giga Design Automation Co. Ltd. Shenzhen China

Logic function decomposition is critical to logic synthesis. In this paper, we propose an integrated logic function decomposition flow, which consists of disjoint support decomposition, exact synthesis, Shannon decomposition, and the recently proposed majority-of-three (MAJ) decomposition. Through this process, the logic function can be decomposed into any directed acyclic graph (DAG). An experimental evaluation shows that our algorithm leads to better XOR-majority graphs (XMGs) compared to state-of-the-art algorithms. Our experiments show that the proposed method achieves an average of 5% and 6% reduction in the lookup tables (LUTs) size/depth product applied to the EPFL arithmetic and random control benchmarks after LUT mapping, respectively.

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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Radar emitter signal recognition based on convolutional neural network and main ridge coordinate transformation of ambiguity function 4

Radar emitter signal recognition based on convolutional neur...

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4th IEEE Advanced Information Management, Communicates, Electronic and automation Control Conference, IMCEC 2021

作者： Pu, Yunwei Liu, Taotao Wu, Haixiao Guo, Jiang Kunming University of Science and Thechnology Faculty of Information Engineering and Automation Kunming650500 China Computer Center Kunming University of Science and Thechnology Kunming650500 China

ISBN: (纸本)9781728185347

Aiming at the time-consuming and inconspicuous features of artificially extracting radar emitter signal features, a recognition method based on deep learning convolutional neural network and ambiguity function main ridge coordinate transformation is proposed. The method extracts the main ridge of the ambiguity function of the signal through fast discrete fractional Fourier transform, and then uses the two-dimensional time-frequency diagram of the main ridge polar coordinate domain of the ambiguity function as the input of the convolutional neural network to realize the sorting and recognition of different radar signals. Experiments show that the method in this paper not only maintains a 100% recognition rate above 0dB, but also stabilizes the recognition accuracy rate above 90% at -6dB. Compared with traditional radar signal recognition methods and other deep learning model recognition methods, the proposed method has greatly improved recognition rate and robustness, and has certain engineering application value. © 2021 IEEE.

关键词： Convolutional neural networks

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Overview of Load Disaggregation Models, Methods and Hardware

Overview of Load Disaggregation Models, Methods and Hardware

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International Universities Power Engineering Conference (UPEC)

作者： Mircea N. Lancranjan Alexandru Muresan Dacian I. Jurj Alexandru Berciu Alexis Polycarpou Levente Czumbil Dan D. Micu Department Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated.

关键词： Load monitoring Costs Scalability Signal processing algorithms Hardware Smart meters Energy efficiency Transient analysis Time complexity Load modeling

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