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customized convolutional neural network for Breast Cancer Classification

SN Computer Science 2024年第2期5卷 207页

作者： Kadadevarmath, Jyoti Reddy, A. Padmanabha Vijayanagara Sri Krishnadevaraya University Karnataka Ballari 583101 India

The deep convolutional neural networks are most trustable and reliable approach to solve any kind of problems in these days. Especially, in recent days’ mammography image analysis uses deep neural network to identify the early stage cancer and it became most acceptable over other machine learning algorithms. These approaches are supporting radiologists to detect suspicious mass variability and other key characteristics in mammography images with good accuracy through greater performance. However, the performance and accuracy of these computer-aided breast cancer detection systems depends on factors, such as quality mammography images, selections of DNN architecture with appropriate hyper parameters. In this work, proposed a new customized convolutional neural network architecture to identify cancerous and healthy breast using mammography images obtained from digital database of screening mammography. The proposed model performed efficiently when compared with other previously proposed models in detection. Cancerous and healthy measured in terms of accuracy and area under curve (AUC) with optimized parameters and cost. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computer-aided breast cancer detection convolutional neural networks (CNNs) customized convolutional neural network Mammography image analysis Optimized parameters

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RESEARCH ON ALGORITHM OF COMPOSITE MATERIAL PAINTING CREATION BASED ON IMAGE PROCESSING TECHNOLOGY

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 4796-4803页

作者： Wang, Yan Wang, Wei Yanshan Univ Sch Art & Desigin Qinhuangdao 066000 Peoples R China

In the study we proposed a novel approach is called a customized convolutional neural network (CCNN) to innovate in the field of art creation, particularly in composite material paintings. This research harnesses the power of image processing technology to analyze and synthesize various artistic elements, thereby facilitating the creation of composite material paintings. The core of the study revolves around the development of a unique algorithm that enables the integration of diverse materials and textures into a cohesive artistic expression. The customized CNN is trained on a vast dataset of images, encompassing a wide spectrum of textures, colors, and patterns, representative of different materials commonly used in art. The network learns to identify and replicate the aesthetic qualities of these materials, thereby empowering artists to explore new realms of creativity. The algorithm not only recognizes the distinct characteristics of each material but also understands how to blend them effectively, maintaining artistic coherence. The results are evaluated to prove proposed performance.

关键词： customized convolutional neural network Composite Material Paintings Image Processing Technology Artistic Creation Texture Analysis Digital Art Innovation

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