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From black box AI to XAI in neuro-oncology: a survey on MRI-based tumor detection

Discover Artificial Intelligence 2025年第1期5卷 1-21页

作者： Asmita Mittal, Praveen Department of Computer Engineering and Application GLA University Uttar Pradesh Mathura Bharthia India

Brain tumor detection remains a critical focus in neuro-oncology, requiring precise and efficient diagnostic methods to support timely clinical decisions. Magnetic Resonance Imaging (MRI) is the modality of choice for this task, owing to its exceptional soft tissue contrast and spatial resolution. Recent advancements in deep transfer learning have driven transformative progress in automating tumor detection, achieving notable improvements in accuracy, scalability, and computational efficiency. This paper systematically reviews state-of-the-art transfer learning techniques, including pre-trained neural networks, domain adaptation approaches, fine-tuning methodologies, and ensemble learning frameworks. It also provides an in-depth analysis of widely used MRI datasets, highlighting persistent challenges such as data imbalance, overfitting, and variability across imaging domains. Emerging trends in model optimization, architectural innovation, and generalization strategies are discussed, offering a forward-looking perspective on the future of automated tumor detection. Through a detailed comparative evaluation, this review identifies the strengths and limitations of existing methods, while outlining potential directions for research aimed at developing robust, interpretable, and clinically deployable systems for brain tumor diagnosis. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Deep Learning Driven X-ray Classification for Pneumonia Detection Using CNN 3

Deep Learning Driven X-ray Classification for Pneumonia Dete...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Saxena, Sarvagya Khandelwal, Siddhartha Chaudhary, Sneha Gupta, Priyanka Gla University Computer Science Engineering Vrindavan India Gla University Computer Science Engineering Mathura India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798331528201

Major deaths (55% or 1.5 million) of LRI (Lower Respiratory Infections) are caused due to Pneumococcal pneumonia. LRI is the 5th highest cause of demise as mentioned by the Global Burden of Disease Study 2015. Health organization suffers from a lack of Physicians. A Physician handles so many cases which leads to misdiagnoses. To resolve all these issues we came up with an easy-to-handle, and implemented in health organizations, the CNN model. The main aim of this model is to help the healthcare professionals by providing them with a tool which will help them detect Pneumococcal pneumonia. With the help of this tool, the work strain of a doctor can be reduced. This model is developed without any pre-trained model and for better accuracy, an immense dataset is used. All the X-ray images were pre processed before being introduced to the neural network. Different layers have been used to make the model more precise and make it adaptable for X-ray images under different conditions. The aim is to combine deep learning concepts with healthcare to make a positive and meaningful impact in the healthcare industry. © 2024 IEEE.

关键词： CNN Models Deep Learning Healthcare Pneumococcal pneumonia X-Ray

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Automated Landslide Detection using Ensemble Learning

Automated Landslide Detection using Ensemble Learning

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Tanishka Agrawal, Subhash GLA University Dept. of Computer Engineering & Application Mathura India

ISBN: (纸本)9798350378092

Landslides pose significant threats to human life, infrastructure, and the environment, making timely detection crucial for effective disaster management. This research paper introduces a novel approach to landslide detection utilizing Convolutional Neural Networks (CNNs) in conjunction with ensemble learning techniques. Leveraging the inherent capabilities of CNNs to extract discriminative features from remote sensing imagery, multiple CNN models are trained and aggregated into an ensemble. Through extensive experimentation on diverse satellite imagery datasets, the effectiveness of the ensemble model is evaluated against standalone CNNs and traditional methods. Results demonstrate that the ensemble approach outperforms individual CNN models and baseline methods regarding accuracy and robustness across varying environmental conditions, enhancing landslide detection capabilities and contributing to more effective disaster management strategies. Our Model achieves 95% Precision, 91% Recall, and 93% f1-score which makes it better than all comparative methods. After classification, we make predictions through GUI by uploading images, in which we are using the Nepal dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Brain Tumor Radiogenomic Classification using multilevel contrastive information fusion 6

Brain Tumor Radiogenomic Classification using multilevel con...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Asmita Mittal, Praveen Department of Computer Engineering Application GLA University Uttar Pradesh Mathura281406 India

Brain tumors present significant challenges in diagnosis and treatment due to their complex and diverse nature. Radiogenomic analysis, which combines genomic and radiological data, has become important for advancing brain tumor research, with deep learning methods, particularly fusion-based approaches, making notable progress. However, current approaches face challenges in capturing both sequential and spatial features, as well as selecting effective fusion techniques. The research introduces a multi-fusion framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers using contrastive learning to enhance feature representation. This approach processes medical images through a neural network alongside a transformer backbone, merging outputs at various stages to improve dark knowledge differentiation between classes. While CNNs extract spatial features, transformers focus on patch-specific information, collectively boosting the model's discriminative power. The method demonstrates a 2-3% performance improvement over state-of-the-art models, with significant advancements in glioblastoma diagnosis and treatment. An ablation study further confirms that each component positively contributes to the classification task. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Convolutional neural networks

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Efficient net-based deep learning: A deep-fake image detection 3

Efficient net-based deep learning: A deep-fake image detecti...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Rohan Sharma, Dilip Kumar Kumar Jain, Praphula Gla University Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Application Mathura India Mits Gwalior Department of Computer Science and Engineering MP India

ISBN: (纸本)9798331528201

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10. © 2024 IEEE.

关键词： Deep Learning Deepfakes Efficient Net Fake Image Video

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IoT based dual health monitoring device for liquid infusion and urine level measurement of urine bag 3

IoT based dual health monitoring device for liquid infusion ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumar, Amit Pathak, Pooja Dwivedi, Vijay Kumar Sharma, Dilip Kumar Gla University Upcst Mathura India Gla University Dept. of Computer Engineering and Application Mathura India Gla University Dept. of Mechanical Engineering Mathura India

ISBN: (纸本)9798331528201

Advanced healthcare monitoring devices that improve patient care through real-time data collecting, remote monitoring, and individualized therapy have been developed as a result of the integration of IoT technology in the healthcare industry. Vital indications including heart rate, blood pressure, glucose levels, and oxygen saturation may be continuously monitored thanks to these IoT-enabled gadgets, which include wearable sensors, smart implants, and remote monitoring systems. These devices assist in the early detection of potential health issues, enabling timely intervention. They help reduce hospital readmission rates by monitoring patients remotely. Additionally, they enhance chronic disease management by transmitting real-time data to healthcare specialists. Furthermore, by providing feedback and reminders, IoT-based healthcare devices encourage patient participation and adherence to treatment programs. Despite the many advantages, IoT technology's role in healthcare monitoring is about to change patient outcomes as it develops. The progress in technology over the years has made it possible to use smaller devices, such as smartwatches mobiles, for health monitoring and disease detection. © 2024 IEEE.

关键词： Blood pressure

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Heart Disease Evaluation with Deep Learning and Machine Learning 6

Heart Disease Evaluation with Deep Learning and Machine Lear...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Upadhyay, Sachin Singh, Sanjiv Kumar Goswami, Jayati Krishna Singh, Shiv Shanker GLA University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350346961

All over the world is affected by the disease which is named as heart disease. The main reason behind the heart disease is our busy life, as the person is affected not only by office work but also by personal problems. This mortality rate is too high. We can predict this disease with the help of Machine Learning (ML) and Deep Learning (DL) prediction models. In this paper, to reach the accuracy we worked on three ML & DL models. For this paper we use ML models named as: SVM (Support Vector Machine), LR (Logistic Regression) & Naïve Bayes. DL models named as: CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) & LSTM (Long Short Term Memory). The accuracy obtained in this study is made up of the 85% accuracy of Logistic Regression, the 89% accuracy of SVM, and the 85% accuracy of Naive Bayes. LSTM has an accuracy of 83%, RNN has an accuracy of 91%, and CNN has an accuracy of 83%. The study's findings indicate that the RNN model is the most accurate, coming in at 90%, and from this we can say that it is the best at predicting heart disease.. © 2023 IEEE.

关键词： Support vector machines

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Ontology Based Agriculture Data Mining using IWO and RNN 6

Ontology Based Agriculture Data Mining using IWO and RNN

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Saraswat, Deepak GLA University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350346961

An ontology is a machine-interpretable formal description of domain knowledge. In current years, ontologies have risen to prominence as a key tool for demonstrating domain knowledge and a key element of several knowledge management systems, decision-support systems (DSS) and other intelligent systems including in agriculture. However, a study of the current literature on agricultural ontologies suggests that the majority of research that suggest agricultural ontologies lack a clear assessment mechanism. This is unwanted because this is impossible to assess the value of ontologies in research and practise without well-structured assessment mechanisms. Furthermore, relying on such ontologies and sharing them on the Semantic Web or amongst semantic-aware apps is problematic. This paper presents a framework for selecting appropriate assessment techniques for Ontology Based Agriculture Data Mining utilizing Invasive Weed Optimization (IWO) and Re-current Neural Network (RNN) that appears to be absent from most recent agricultural ontology research. The framework facilitates the selection of relevant evaluation techniques for a particular ontology based on its intended user.. © 2023 IEEE.

关键词： Data mining

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Activity Recognition System via Unification of CNN and SVM in Complex Domain 6

Activity Recognition System via Unification of CNN and SVM i...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Choudhary, Parul Pathak, Pooja Chaubey, Abhishek GLA University Department of Computer Application and Engineering Mathura India

ISBN: (纸本)9798350346961

Human automatic activity recognition is an essential part of a little interactive application which involves human-being. The main disadvantage is the person's diverse activities. These techniques provide high accuracy and pattern recognition. Deep learning methods succeed in human activity recognition (HAR);hence our goal is to combine Convolutional Neural Networks (CNN), which work with image data and extract features from images with Support Vector Machines (SVM), a shallow architecture that maps low-dimensional space to high-dimensional space. This paper uses an intelligent system algorithm to detect human activity and recognize its pattern. Cooking, cleaning, dancing, steering, and discussion activities are to be tested. Each class has 7480 images. Applying all preprocessing techniques, experimental results shows that the cleaning class gives the highest training and testing accuracy i.e., 98.78% and 97.51%. This method achieves the highest recognition accuracy with the lowest computational cost.. © 2023 IEEE.

关键词： Support vector machines

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Deep learning for deepfake detection: Scientific landscape and future directions 4th

Deep learning for deepfake detection: Scientific landscape a...

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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Dhiman, Pummy Kaur, Amandeep Sharma, Dilip Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Engineering and Application GLA University Mathura India

ISBN: (纸本)9781032911571

In today’s digital age, technology has advanced to the point where it is difficult to distinguish be-tween genuine and forged media content. It was intended to be used for entertainment, but it is now being used to deceive people and create biasness, which can lead to disastrous situations. The goal of this bibliometric study is to provide scientific mapping on the role of deep learning in deepfake detection published online. Scopus and VOSviewer are used to create a concept map of 128 articles. Additionally, future directions for research are suggested. © 2025 the Author(s).

关键词： Fake detection

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