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AI Recognition in Skin Pathologies Detection

AI Recognition in Skin Pathologies Detection

引用

International conference on artificial Intelligence applications and Innovations (IC-AIAI)

作者： Dmitriy Gavrilov Lyubov Lazarenko Emil Zakirov Moscow Institute of Physics and Technology Dolgoprudniy Russia

ISBN: (数字)9781728143262

ISBN: (纸本)9781728143279

Skin cancer is the most common type of cancer [1]. Between different malignant skin pathology melanoma is the most fleeting and mortality. Despite the superficial location of pathologies, only half of patients seek medical assistance on the early stages[2]. Treatment on the early (epidermal) stage provides a significantly higher chance of recovery. To assist a wide range of people in the early skin cancer detection, a software package was developed. The software based on deep convolutional neural networks technology. This complex allows to classify normal and malignant pathology on the uploaded photos. In clinical practice doctors use the ABCDE symptom's complex. This complex characterizes the observation of pigment spot asymmetry, border irregularities, color unevenness, diameter, and evolution [3]. The machine learning approach involves the computer evaluating similar factors when processing multiple images of different skin formations. The paper presents an algorithm for classification of skin lesions into pathology and norm using convolutional neural network architecture Xception with prior images segmentation. The upper classifying layers were frozen and new ones were added to classify skin diseases in the pre-trained neural network Xception. As a result, the classification of benign and malignant skin tumors provided at least 89% accuracy. At the moment, the result of research work is designed in form of application software that allows to download the image of pigmented skin spots from the camera. It is available on https://***.

关键词： Skin Pathology Melanoma Training image color analysis

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Fruit Tree Disease Recognition Based on Convolutional neural networks

Fruit Tree Disease Recognition Based on Convolutional Neural...

引用

IEEE International Symposium on Parallel and Distributed processing with applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zechen Zheng Shaowei Pan Yichi Zhang School of Computer Science Xi’an Shiyou University xi’an China China

ISBN: (纸本)9781728152103

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution neural Network models are used to predict test sets, and the results show that deeper Convolution neural networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.

关键词： agricultural products convolutional neural nets feature extraction image enhancement image recognition learning (artificial intelligence) plant diseases image enhancement image recognition agricultural products agricultural byproducts fruit trees Feature extraction Sobel operator Recognition (Psychology) Convolution maladjustment Dataset neural network Plant Diseases tree diseases Prunus persica

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Transfer Learning Based Histopathologic image Classification for Burns Recognition

Transfer Learning Based Histopathologic Image Classification...

引用

International conference on Electronics, Computer and Computation (ICECCO)

作者： Aliyu Abubakar Hassan Ugail Ali Maina Bukar Ali Ahmad Aminu Ahmad Musa Centre for Visual Computing University of Bradford Bradford United Kingdom Computer Science Department Nile University of Nigeria Abuja Nigeria Faculty of Engineering and Informatics University of Bradford Bradford United Kingdom

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151618

Burn is one of the most leading devastating injuries affecting people worldwide with high impact rate in low-and middle-income countries subjecting hundreds of thousands to loss of lives and physical deformities. Both affected individuals and health institutions are faced with challenges such as inadequate experience/well trained workforce and high diagnostics cost. The demand of having efficient, cost-effective and user-friendly technique to aid in addressing the problem is on the rise. Deep neural networks have recently attracted the attention of many researchers and achieved impressive results in many applications. Therefore, this paper proposed the use of off-the-shelf Convolutional neural Network features from two imageNet pre-trained models (GoogleNet and ResNet152), VGG-Face. The features are used to train Support Vector Machine (SVM) and Decision Tree (DT). 100% identification accuracy was recorded using imageNet model and SVM.

关键词： biological tissues convolutional neural nets feature extraction image classification learning (artificial intelligence) medical image processing image classification Medical image processing TISSUES (BIOLOGY) Feature extraction Burns Support Vector Network Transfer (Psychology)

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Hardware Architecture for FPGA Implementation of a neural Network and Its Application in images processing

Hardware Architecture for FPGA Implementation of a Neural Ne...

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Electronics, Robotics and Automotive Mechanics conference

作者： Barba J. Leiner Vargas Q. Lorena Torres M. Cesar Mattos V. Lorenzo Grupo de Óptica e Informática Universidad Popular del Cesar Colombia

ISBN: (纸本)9781424432455

This work describes a hardware architecture implementation of an associative memory neural network (AMNN) using reconfigurable hardware devices such as FPGA (field programmable gates arrays) and its applications in image pattern recognition systems. An associative memory is a content-addressable structure that maps specific input representations to specific output representations. It is a system that "associates" two patterns (X, Y) such that when one is encountered, the other can be recalled. In the design, learning and recognizing algorithms for the neural network are implemented by using VHSIC Hardware Description Language. FPGA is used for implementation because they can reduce development time greatly, ease of fast reprogramming, low price, flexible architecture and permitting fast and non expensive implementation of the whole system. The architecture was evaluated as image recognizing system. Likewise, it was necessary to implement and acquisition stage.

关键词： artificial neural networks Field programmable gate arrays Associative memory Hardware Neurons Pixel Random access memory

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Insights Into YOLOv9-Based Breast Mass Detection Using Ultrasound images

Insights Into YOLOv9-Based Breast Mass Detection Using Ultra...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Nouhaila Erragzi Abdelghani Moussaid Nabila Zrira Ibtissam Benmiloud Rajaa Sebihi Nabil Ngote Faculty of Science in Rabat LPHE-Modeling and Simulations Mohammed V University Morocco MECAtronique Team CPS2E Laboratory National Superior School of Mines Rabat Morocco ADOS Team LISTD Laboratory National Superior School of Mines Rabat Morocco Abulcasis International University of Health Sciences National Superior School of Mines Rabat Morocco

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

The most prevalent form of cancer globally is breast cancer, which predominantly impacts women. Early detection ensures successful treatment of breast cancer, significantly improving patients' survival chances. Various imaging modalities, including mammography and ultrasound, are utilized for breast cancer screening. Incorporating new technologies is essential for better patient management, particularly for those with malignant masses. artificial intelligence can assist radiologists by training neural networks to detect breast lesions on mammograms or ultrasounds using deep learning techniques. In this article, the YOLOv9 network is trained on two public ultrasound databases, UDIAT and BUSIS. The network successfully localized malignant and benign masses with a precision of 83%, a recall of 82%, and a mAP of 87% in the UDIAT dataset. In the BUSIS dataset, our model achieved a precision of 75%, a recall of 88%, and a mAP of 90%. Furthermore, we used real Moroccan cases to evaluate the model's performance.

关键词： Training Deep learning Ultrasonic imaging Databases image processing neural networks Imaging Breast cancer Mammography Lesions

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Research on Inverted Pendulum Control Based on Cascade Fuzzy Control and neural Network Control

Research on Inverted Pendulum Control Based on Cascade Fuzzy...

引用

image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Shengkai Wang School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In this paper, the problem of precise control of single-stage inverted pendulum system is deeply discussed and practiced. The dynamic modeling and simulation of the inverted pendulum system were carried out in the course of the experiment, and the whole process of the inverted pendulum control was vividly reproduced by visual means, which enhanced the intuitiveness and effectiveness of the model design and control strategy analysis. In the design of control strategy, two advanced methods of neural network control and cascade fuzzy control are combined. The neural network controller uses its powerful nonlinear mapping ability and self-learning characteristics to approximate the complex dynamic behavior of the inverted pendulum system in real time and accurately. The cascade fuzzy controller realizes the hierarchical fine control of the Angle and angular velocity of the inverted pendulum through the upper and lower two-level control system, which improves the stability and robustness of the control system. After a series of rigorous experimental testing and optimization adjustment, the control output curve and the response curve of inverted pendulum state under different control methods are obtained successfully. Through the comparative analysis of these results, the advantages of the comprehensive scheme based on cascade fuzzy control and neural network control in improving the stable equilibrium time of inverted pendulum, reducing the overthrow amplitude and enhancing the anti-disturbance ability are revealed, and the effectiveness and superiority of the control strategy are verified, providing a new idea and reference for the theoretical research and practical application of inverted pendulum control.

关键词： Fuzzy control Analytical models Visualization neural networks Process control Stability analysis Robustness Real-time systems Angular velocity Testing

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Energy-Efficient Ternary Multiplier

Energy-Efficient Ternary Multiplier

引用

IEEE International conference on artificial Intelligence Circuits and Systems (AICAS)

作者： Rishi Agrawal Narayanabhatla Savyasachi Abhijith Uppugunduru Anil Kumar Sreehari Veeramachaneni Syed Ershad Ahmed Department of Electrical and Electronics Engineering BITS Pilani Hyderabad Telangana India Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education (Deemed to be University) Hyderabad Telangana India Department of Electronics and Communication Engineering GRIET Hyderabad Telangana India

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Multiplication is a fundamental arithmetic operation with applications in various fields, such as neural networks, signal and image processing. Ternary multiplication, an emerging area of research, explores the multiplication of numbers represented in the ternary numeral system. This paper proposes a new technique for computing partial product trits of multi-digit multiplication, along with improved adder modules, intending to reduce power dissipation. Two parallel paths have been proposed to calculate the final product, thereby improving the latency of the multiplier. The internal adder modules of the multiplier have been designed to optimise the number of transistors, resulting in lower power consumption. Results prove that the proposed design significantly improves power, delay, and the power-delay product (PDP). The proposed three-trit multiplier shows a 20.7% improvement in power, 23.17% in delay, 39.11% in PDP, and 18.76% in transistor count compared to the existing design.

关键词： Power demand Circuits and systems image processing neural networks Energy efficiency Delays Power dissipation

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Improving CNN-based colorization of B&W photographs

Improving CNN-based colorization of B&W photographs

引用

International image processing, applications and Systems conference (IPAS)

作者： Sanae Boutarfass Bernard Besserer MIA Laboratory University of La Rochelle LA ROCHELLE France

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Colorization is the process of converting a black and white image - lot of different shades of gray - into a realistic colour image. Our contribution highlights the fact that the CNNs used for colorization (and specifically the data set used for their training) are not adequate to colourize legacy B&W pictures. In fact the data sets are exclusively composed of colour images, and turning these colour images into greyscale pushes the CNN to learn the contribution of each colour to the resulting luminance. Anyway, CNN are quite good in classification task and can easily recognize skies, trees and foliage, faces and persons. The colorization works well for these elements, but it fails even on memorable objects such as flags and well-known monuments albeit their representation is present in the data sets. We make improvement by using a pretrained network and add hints in the colorization process. Global hints (a colour palette given to the CNN conjointly to the image) still leave a high degree of automation, and these hints could be provided once for a collection of images related to the same theme, which is very suitable for movie colorization. Adding manual hints by scribbling colours onto the B&W image leads to even better results but needs user interaction.

关键词： image color analysis Vegetation mapping artificial neural networks Vegetation Transforms Motion pictures Turning

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Mathematics-Inspired Learning: A Green Learning Model with Interpretable Properties *

Mathematics-Inspired Learning: A Green Learning Model with I...

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IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Ling Guan Lei Gao Kai Liu Zheng Guo Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

The advances of machine learning (ML), and artificial intelligence (AI) in general, have attracted unprecedented attention in different applications (such as image classification, video computing, natural language processing, etc.). Nevertheless, due to the black-box nature and concern for sustainability of ML models, especially deep neural networks (DNNs), green and interpretable learning have been extensively studied in recent years, despite suspicions on effectiveness, subjectivity of interpretability and complexity. To address some of the concerns, this paper first explores a mathematics-inspired (M-I) learning model, which is green in nature with numerous interpretable properties. It will then present several application examples in object recognition and action recognition to demonstrate that the M-I methodology promises a plausible and sustainable path for natural evolution of machine learning.

关键词： Computational modeling Closed box Machine learning Information processing Mathematical models Natural language processing Complexity theory Object recognition Sustainable development image classification

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Evolving deep neural networks: A new prospect

Evolving deep neural networks: A new prospect

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International conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Sreenivas Sremath Tirumala S Ali C Phani Ramesh Auckland University of Technology Auckland New Zealand UNITEC Institute of Technology Auckland New Zealand Sri Venkateswara University Tirupati India

The success of Deep neural networks (DNNs) for various applications like language processing (NLP), image processing, character recognition inspired to use machine learning (ML) and Evolutionary Computation (EC) techniques for improving learning process. Using evolutionary algorithms to improve the efficiency of deep learning attained some success. However, these techniques are unable to reduce the learning time which is the key concern for deep learning. The main problem with DNN is that, it uses a random topology to start with (similar to artificial neural networks). If the topology is not suitable, training procedure will restart with a new topology and this process continues till expected results are obtained. In this paper, we propose, for the first time, a new prospect for evolving optimized deep neural networks which can provide a warm start to the training process compared to heuristic random initial architecture. We discuss the theoretical approach towards possibility of optimizing the learning process inspired from the existing un-conventional approaches. The training process of DNN with EC approach is faster than regular approach by a considerable difference of over 6 hours for MNIST data set. Further, we observed a considerable improvement in the classification accuracies. Our approaches resulted in an improved classification accuracy of 2% and 4.4%for MNIST data set and 1.2% and 1.4% for IRIS data set compared to heuristic random weights approach. Our initial experimental results prove that evolutionary approaches provides a warm start to the deep learning, thus, reducing the training time.

关键词： Training Machine learning neural networks Computer architecture Network topology Topology Encoding

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