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Advancing Alzheimer's Diagnosis Through Transfer Learning with Deep MRI Analysis

Advancing Alzheimer's Diagnosis Through Transfer Learning wi...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Adityasai, B. Babu, Nirmal Varghese Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350371567

Alzheimer's disease (AD) poses a significant health-care challenge, necessitating early and accurate diagnosis for effective management. This study presents a novel approach aimed at enhancing AD diagnosis through the integration of transfer learning, deep learning techniques, and meticulous data preprocessing methodologies. Leveraging a rich dataset from the Open Access Series of Imaging Studies (OASIS), comprising MRI scans from both healthy individuals and those with AD, the proposed method seeks to identify subtle abnormalities indicative of the disease. The methodology encompasses several key steps. Initially, MRI data undergoes rigorous cleaning and standardization using histogram equalization to ensure compatibility and remove irrelevant information. Subsequently, a two-step feature extraction process involving an autoencoder neural network is employed to identify significant AD-related features. These features are then combined with other relevant data to provide a comprehensive understanding of the disease. To optimize model efficiency, three algorithms iteratively refine the feature set, focusing on the most informative features associated with AD. Finally, a pretrained deep learning model, VGG19, is fine-tuned using the AD-specific dataset. Results demonstrate a significant improvement in diagnostic accuracy post-hyperparameter tuning, with accuracy increasing from 76.5% to 89.5%. The enhanced model performance across various metrics underscores the potential of the proposed approach to revolutionize AD diagnosis, offering promise for earlier detection and more accurate prognostication. This research contributes to advancing the field of neuroimaging-based diagnostic techniques for neurodegenerative diseases like Alzheimer's. © 2024 IEEE.

关键词： Magnetic resonance imaging

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of Handwritten Image-To-Speech Using Deep Learning

Investigation of Handwritten Image-To-Speech Using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Manju, S. Anitha, J. Sujitha Juliet, D. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

In the realm of accessibility technology, this paper introduces a pioneering method for converting handwritten images to speech. The work primarily focuses on recognizing handwritten text and subsequently converting it into audible speech. The work comprises two fundamental phases: initially, a deep learning algorithm is employed to recognize and extract text from handwritten images precisely. Following this, the recognized text is converted into speech using the pyttsx3 library which offers flexibility in voice selection, speech rate, and other parameters, making it highly customizable for different applications. To ascertain the most effective algorithm for the text recognition phase, a comparative analysis is conducted among the top three algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models - based on their accuracy in recognizing handwritten text. Experimental results indicate that CNNs surpass the other two algorithms, achieving an accuracy of 92%. This work offers a pragmatic approach to converting handwritten content into speech, with potential applications in aiding the visually impaired and enhancing accessibility to written information. © 2024 IEEE.

关键词： Long short-term memory

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A Deepfake Image Classifier System for real and Doctored Image Differentiation 7

A Deepfake Image Classifier System for real and Doctored Ima...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thomas, Juanith Mathew Ebenezer, V. Richard, Rohan Paul Karunya Institute of Technology And Sciences Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Sciences Division of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350359299

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet"architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: dataset I comprises authentic images while dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we'll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%. © 2024 IEEE.

关键词： Digital storage

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Knee Osteoarthritis Detection Using X-Rays and DNN 3

Knee Osteoarthritis Detection Using X-Rays and DNN

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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Machine Learning for Detecting Subtle Signs of EyeDisease 4

Machine Learning for Detecting Subtle Signs of EyeDisease

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Dafny Neola, J. Ebenezer, V. Edwin, E.Bijolin Kirubakaran, S.Stewart Thanka, M.Roshni Yona Davincy, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350385793

Eye disorders can have serious repercussions, such as blindness or visual impairment, if they are not identified or addressed, Treatment that works and preventing permanent harm depend on early diagnosis and prediction. Using cutting-edge clinical observations, this method looks for diseases such diabetic retinopathy, cataracts, myopia, glaucoma, age-related eye illnesses, and hypertension-related eye disorders. This study presents an innovative method for early detection of eye health disorders: The Eye Health Station. It uses machine learning algorithm, by leveraging EfficientNet's superior performance in image classification tasks, our system analyzes ocular images, such as retinal scans, to identify potential indicators of eye disorders at an early stage., The proposed system seeks to discover possible markers of eye problems by evaluating several factors taken from ocular pictures and patient data, including retinal scans and medical history. The system is able to learn from various datasets and gradually increase its accuracy since machine learning techniques are included into the system. © 2024 IEEE.

关键词： Ophthalmology

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VGG-16 based Deep Learning Approach for Plant Species Detection

VGG-16 based Deep Learning Approach for Plant Species Detect...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Praveen, Bandi Isravel, Deva Priya Malar Dhas, Julia Punitha Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

Automated plant species detection has gained interest due to the potential benefits in agriculture to significantly increase crop yields, decrease the use of chemicals, and promote sustainable agriculture practices. A deep learning model, for plant species identification is a promising approach. This paper presents a condensed version of the VGG-16 deep learning model for identifying plant species. The original VGG-16 model's complexity and computational overhead are minimized by lowering the number of convolutional layers, which lowers the amount of parameters and calculations needed while keeping a high level of accuracy in the identification of various plant species. The streamlined VGG-16 model obtains an improved accuracy rate in categorizing distinct plant species after being trained on a big dataset of plant species that includes a variety of images of different plants. The condensed VGG-16 model offers a viable method for precise and effective plant species identification. This study's methodology and results advances the area of plant species detection by offering a deeper learning model that is more effective and useful and can be used on devices with limited resources. © 2024 IEEE.

关键词： Agriculture

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Diagnosis of Plant Leaf Disease using Vision Transformer 10

Diagnosis of Plant Leaf Disease using Vision Transformer

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sundaraj, Ashik Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning based Intelligent System for Skin Disease Detection 10

Deep Learning based Intelligent System for Skin Disease Dete...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Dhanush, K.B. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamilnadu Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Tamilnadu Coimbatore India

ISBN: (纸本)9798350353068

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of 91.3 % and 95.6 %, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience. © 2024 IEEE.

关键词： Diagnosis

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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