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A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours Using Different Modalities

ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年第2期9卷 1216-1225页

作者： Sahu, Adyasha Das, Pradeep Kumar Paul, Indraneel Meher, Sukadev VIT Bhopal Univ Sch Comp Sci Engn & Artificial Intelligence Bhopal 466114 India Vellore Inst Technol Sch Elect Engn Vellore 632014 India Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 India

Nowadays, deep convolutional neural networks (DCNNs) are the focus of substantial research for classification and detection applications in medical image processing. However, the limited availability and unequal data distribution of publicly available datasets impede the broad use of DCNNs for medical image processing. This work proposes a novel deep learning-based framework for efficient detection of brain tumors across different openly accessible datasets of different sizes and modalities of images. The introduction of a novel end-to-end Cumulative Learning Strategy (CLS) and Multi-Weighted New Loss (MWNL) function reduces the impact of unevenly distributed datasets. In the suggested framework, the DCNN model is incorporated with regularization, such as DropOut and DropBlock, to mitigate the problem of over-fitting. Furthermore, the suggested augmentation approach, Modified RandAugment, successfully deals with the issue of limited availability of data. Finally, the employment of K-nearest neighbor (KNN) improves the classification performance since it retains the benefits of both deep learning and machine learning. Moreover, the effectiveness of the proposed framework is also validated over small and imbalanced datasets. The proposed framework outperforms others with an accuracy of up to $ 99.70\%$.

关键词： Tumors Databases Accuracy Magnetic resonance imaging Training Brain modeling Nearest neighbor methods Brain tumors convolutional neural network data augmentation deep learning machine learning MRI

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Difficulty-Aware Dynamic Network for Lightweight Exposure Correction

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第6期34卷 5033-5048页

作者： Li, Ziwen Shao, Yuanjie Zhang, Feng Zhang, Jinpu Wang, Yuehuan Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.

关键词： image enhancement exposure correction difficulty awareness dynamic convolution

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Generative Artificial intelligence for Hyperspectral Sensor Data: A Review

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution Machine learning Neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

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Guest Editorial: Special Issue on Emerging computational intelligence Techniques to Address Challenges in Biomedical Data and Imaging

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2023年第2期7卷 292-294页

作者： Tanveer, M. Lin, Chin-Teng Ting, Chuan-Kang Andreu-Perez, Javier Indian Inst Technol Indore Dept Math Simrol India Univ Technol Sydney Computat Intelligence & Brain Comp Interface Lab Ultimo NSW 2007 Australia Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 300 Taiwan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

The papers in this special section focus on emerging computational intelligent techniques to address the challenges in biomedical data and image processing.

关键词： Special issues and sections Biomedical imaging computational intelligence Computed tomography computational modeling Bioinformatics Deep learning Data mining

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Coarse-to-Fine Low-Light image Enhancement With Light Restoration and Color Refinement

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2024年第1期8卷 591-603页

作者： Wu, Xu Lai, Zhihui Yu, Shiqi Zhou, Jie Liang, Zhuoqian Shen, Linlin Shenzhen Univ Comp Vis Inst Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SZU Branch Shenzhen 518060 Peoples R China Coll Shandong Future Networks Res Inst Jinan 250002 Shandong Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

Low-light image enhancement aims to improve the illumination intensity while restoring color information. Despite recent advancements using deep learning methods, they still struggle with over or under-exposure in complex lighting scenes and poor color recovery in dark regions. To address these drawbacks, we propose a novel pipeline (called LRCR-Net) to perform Light Restoration and Color Refinement in a coarse-to-fine manner. In the coarse step, we focus on improving the illumination adaptively while avoiding inappropriate enhancement in the brighter or darker regions. This is achieved by introducing a region-calibrated residual block (RCRB) that balances local and global dependencies among different image regions. In the fine step, we aim to retouch the color of the images enhanced in the coarse step. To achieve this goal, we propose learnable image processing operators (LIPOs), including contrast and saturation operators, to refine the color according to the input images' color and contrast information. The final result is an image with proper illumination and rich color. Experiments on four benchmark datasets (NASA, LIME, MEF, and NPE) show that our model outperforms state-of-the-art methods.

关键词： Low-light image enhancement transformer learnable image processing operators

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Eggs Separation Process by image processing. 18

Eggs Separation Process by Image Processing.

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18th International Joint symposium on Artificial intelligence and Natural Language processing (ISAI-NLP) / 4th International Conference on Artificial intelligence and Internet of Things (AIoT)

作者： Chumuang, Narumol Pinthong, Thidarat Muban Chombueng Rajabhat Univ Dep Digital Media Technol Ratchaburi Thailand Phetchaburi Rajabhat Univ Dep Business Informat Management Phetchaburi Thailand

ISBN: (纸本)9798350371215

The main objective of this research is the process of sorting chicken eggs using image processing techniques. The objective is to measure the size of chicken eggs. To control the separation of chicken eggs in the experiment, the researchers used the Edge Detection technique. The experiment took pictures of chicken eggs of different sizes, and the system was able to separate the eggs correctly.

关键词： Eggs Separation Process image processing Edge Detection

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

2025 IEEE Symposium on Computational Intelligence in Health ...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

ISBN: (纸本)9798331508333

The proceedings contain 17 papers. The topics discussed include: transforming medical practice: harnessing the power of big data and machine learning for predictive precision medicine;algebraic reproduction of triage decision making on simple heuristics of Boolean multivalued logic;deep learning on Hester Davis scores for inpatient fall prediction;comparative analysis and evaluation of well-being activity-infused fine-tuned language models with benchmark models;application of fuzzy-based uncertainty in cardiac MRI segmentation;images of stress: investigating image encodings and transfer learning for stress detection;training deep neural networks with multi-objective Adam optimizer for medical image classification;and deep-learning classifiers for small data orthopedic radiology.

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Automotive Scenarios for Trajectory Tracking using Machine Learning Techniques and image processing 17

Automotive Scenarios for Trajectory Tracking using Machine L...

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17th ieee International symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Moga, Delia Filip, Ioan Politehnica University of Timisoara Department of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9798350321104

This paper presents a study on using innovative machine learning techniques that can be applied in automotive traffic scenarios to increase a vehicle's level of autonomy. The overtaking traffic scenario is treated for predicting the vehicle trajectory when overtaking another vehicle and the data is obtained by image processing using a video camera. Two different methods are compared, first by using classic tracking methods and a Kalman filter (as an adaptive filter) and second by using a machine learning technique - Support Vector Machine. The present article uses as inputs the data received from the camera and focuses on tracking selected objects and estimating their position using mainly image processing in automotive scenarios. The main purpose of this work is to experiment and compare different tracking modes to determine those that have the best performances in terms of runtime, memory usage and prediction accuracy. © 2023 ieee.

关键词： Support vector machines

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JaFIn: Japanese Financial Instruction Dataset

JaFIn: Japanese Financial Instruction Dataset

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2024 Conference on computational intelligence for Financial Engineering and Economics

作者： Tanabe, Kota Suzuki, Masahiro Sakaji, Hiroki Noda, Itsuki Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan Univ Tokyo Sch Engn Tokyo Japan

ISBN: (纸本)9798350354843;9798350354836

We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To achieve this, we propose an instruction tuning data in Japanese called JaFIn, the Japanese Financial Instruction Dataset. JaFIn is manually constructed based on multiple data sources, including Japanese government websites, which provide extensive financial knowledge. We then utilize JaFIn to apply instruction tuning for several LLMs, demonstrating that our models specialized in finance have better domain adaptability than the original models. The financial-specialized LLMs created were evaluated using a quantitative Japanese financial benchmark and qualitative response comparisons, showing improved performance over the originals.

关键词： Financial Natural Language processing Large Language Model Instruction Dataset Instruction Tuning Domain Adaptation

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An Intelligent System With Reduced Readout Power and Lightweight CNN for Vision Applications

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第2期34卷 1310-1315页

作者： Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Indian Inst Technol Jodhpur Elect Engn Dept Jodhpur 342030 India Indian Inst Technol Jodhpur Comp Sci & Engn Dept Jodhpur 342030 India

An always-on intelligent system comprising of an image sensor requires continuous functioning of each pixel. This includes sensing the illumination content of the scene and also the conversion of the analog values into their digital representations. Therefore, power consumption during analog to digital conversion and computational cost at the image sensor module become critical while designing a system that is always-on and incorporates intelligence near the sensor module. This work focuses on the inherent property of the ADC for converting the analog pixel values to digital values by taking a defined number of analog-to-digital converter (ADC) cycles. The design factors considered are 1) Power saving due to reduced ADC conversion cycles for each pixel;2) The reduced bit-precision of the processing unit to reduce hardware cost;3) The dataflow design through hls4ml, which produces parallel computational modes for low latency CNN architectures. The proposed work implements two lightweight CNN models with reduced parameters as compared to the original architectural models of VGG16 (like) and SqueezeNet (like) which are trained in Qkeras and deployed on Zynq UltraScale+ MPSoC board. In addition, the design pipeline is validated on the MobileNetV2 and GhostNet architectures to demonstrate its generalization ability. A detailed analysis shows that limiting the number of ADC bits from 8 to 4 reduces the mean accuracy merely from 50.3 to 49.17 for VGG16 (like) and 67.83 to 67.80 for SqueezeNet (like) model, however, the readout power is significantly reduced from 140.45 mW to 7.7 mW for STL-10 dataset with $96\times96$ image resolution. Additional experiments are conducted with CIFAR-10 and mini-imageNet datasets for classification and with Oxford-IIIT Pet Dataset for segmentation. The proposed work, thus, provides empirical evidence that a reasonable performance for intelligent vision tasks with power saving can be achieved by tuning CNN models to work with reduced ADC bit

关键词： Computer architecture image resolution computational modeling Convolutional neural networks Power demand Task analysis Standards Edge intelligence CMOS image sensors high-level synthesis deep learning

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