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AI in Eye Health: Model Comparison

₹1500-12500 INR

Suljettu
Julkaistu noin 2 kuukautta sitten

₹1500-12500 INR

Maksettu toimituksen yhteydessä
This project aims to conduct a comparative evaluation of two advanced convolutional neural network (CNN) architectures, EfficientNet-V2 and ResNet, for the task of DR classification. The study utilizes two widely recognized datasets: APTOS 2019 Blindness Detection and IDRiD (Indian Diabetic Retinopathy Image Dataset), providing a diverse collection of retinal images annotated for DR severity levels. EfficientNet-V2, known for its balance between accuracy and computational efficiency, is compared against ResNet, a popular CNN model renowned for its strong performance in computer vision tasks. Both architectures are rigorously trained and optimized using techniques like transfer learning and data augmentation to ensure robust performance across the datasets. The models' performance is evaluated on their ability to classify retinal images into five DR severity categories: no DR, mild non-proliferative DR, moderate nonproliferative DR, severe non-proliferative DR, and proliferative DR. Comprehensive metrics are employed to assess the accuracy and reliability of the models' predictions. By comparing the performance of EfficientNet-V2 and ResNet on these diverse datasets, the study aims to provide insights into the suitability and potential of these architectures for DR classification tasks. The findings will contribute to the ongoing research efforts in developing accurate and reliable computer-aided diagnosis systems for diabetic retinopathy, ultimately supporting early detection and improving patient outcomes. **Target Audience:** - Machine Learning Experts - Individuals passionate about healthcare technology **Data for Model Training:** - The project will utilize publicly available datasets to train both EfficientNet-V2 and ResNet models. Accessibility to a variety of datasets ensures a broad and comprehensive training process, essential for achieving high accuracy and reliability in disease classification. **Development of User Interface:** - A straightforward, user-friendly interface will be developed. This UI is intended to display the classification results clearly and concisely, catering to healthcare professionals who may not have deep technical knowledge in machine learning but need to interpret the results effectively in their diagnostic process. **Ideal Skills and Experience for the Job:** - Proficiency in machine learning and deep learning, specifically with experience in EfficientNet-V2 and ResNet models. - Strong background in medical imaging analysis or a keen interest in healthcare applications of machine learning. - Experience in developing simple yet functional user interfaces, preferably with knowledge in UI/UX design principles tailored for medical applications. - Ability to work with publicly available datasets, including data pre-processing and augmentation techniques to improve model training. This project not only promises to advance the field of medical diagnostics through AI but also offers an opportunity to contribute to meaningful health outcomes for individuals affected by Diabetic Retinopathy. If you have the skills and the drive to help realize this vision, I look forward to your bid and embarking on this exciting journey together.
Projektin tunnus (ID): 37782175

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12 ehdotukset
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Aktiivinen 17 päivää sitten

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12 freelancerit tarjoavat keskimäärin ₹8 492 INR tätä projektia
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Hello. I hope you are doing well. I am a PYTHON expert and have 4 years of experiences in ML & DL, Computer Vision & Image Processing. I can provide you the perfect result on time. Please contact me so that we can discuss about the details of the project. Thank you for your time
₹20 000 INR 5 päivässä
5,0 (17 arvostelua)
4,3
4,3
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Hai i saw your requirement related to diabetes retinopathy images dataset. I have already did DR image classification. I think that work contains more than 10 classes. So i will do this work.. i already have experience in many pre trained architecture like efficient Net , resnet ,vgg series ..i will do transfer learning. I will implement this code on Google colab ( python environment). Kindly text.. I can't open your pdf file.. kindly send me...
₹12 000 INR 7 päivässä
4,1 (7 arvostelua)
3,9
3,9
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I'm Divyang Mandani from KriraAI Infotech, excited about your project on the comparative evaluation of EfficientNet-V2 and ResNet for Diabetic Retinopathy (DR) classification. Our strategy involves rigorous training, optimizing both architectures through transfer learning and data augmentation on APTOS 2019 Blindness Detection and IDRiD datasets. We aim to assess their performance across five DR severity categories, providing comprehensive metrics for accuracy and reliability. Targeting Machine Learning Experts and healthcare technology enthusiasts, we plan to use publicly available datasets for broad and comprehensive training, ensuring high accuracy in disease classification. Understanding the significance of user-friendly interfaces for healthcare professionals, our team specializes in developing clear and concise UIs tailored for medical applications, facilitating effective interpretation of classification results. Our ideal skills align with proficiency in machine learning, deep learning using EfficientNet-V2 and ResNet models, a strong background in medical imaging analysis, and expertise in UI/UX design principles for medical applications. This project not only promises advancements in medical diagnostics through AI but also offers an opportunity to contribute to meaningful health outcomes. If you're seeking a dedicated team to bring this vision to life, we're ready to embark on this exciting journey together. Best regards, Divyang Mandani KriraAI Infotech
₹7 000 INR 7 päivässä
5,0 (4 arvostelua)
2,3
2,3
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hi, this is an interesting project , recently I have done a comparison of synthetic data generation tools as part of world wide contest and our team was rank among top 3 , can help you with this project. Could you specify if this is an academic personal project or is it commercial project. Let’s connect sometime soon I can volunteer for this project and will not charge.
₹1 500 INR 7 päivässä
0,0 (0 arvostelua)
0,0
0,0
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With over a decade of experience in data science and analytics, I am Emphasizing the quality of work and client satisfaction. I am distinctly equipped with the expertise needed to successfully execute your AI for Eye Health project and produce reliable results. Having specialized skills in machine-learning (including Python/R) as well as strong exposure to efficientNet-V2 and Resnet models, I have the versatiltiy necessary to assess and compare their performances thoroughly. My practical understanding in medical imaging analysis makes me uniquely positioned to apply your detailed datasets to formulating a promising solution for Diabetic Retinopathy identification. Additionally, anticipating your need for an easily understandable interface to analyze the results, I boast commendable knowledge in developing user-interfaces (Web, Android, IOS etc) with simplistic design principles. Your project's relevance and potential impact aligns perfectly with my values as a professional; I prioritize perfection, simplicity, innovation and marketing in all my endeavors. Joining forces with me ensures not just quality output but a impactful deliverable that can transform diabetic retinopathy diagnoses. Let's take this journey together towards better health outcomes through AI-driven medical technology!
₹10 000 INR 2 päivässä
0,0 (0 arvostelua)
2,4
2,4
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My experience in machine learning includes two successful projects, one involving the development of a gait analysis system for individuals with mobility impairment using advanced ML algorithms. Additionally, I implemented a Hotel Reservation Cancellation Prediction model with a user-friendly interface using Gradio and Tkinter GUI, showcasing my skills in predictive modeling and UI development. These projects demonstrate my proficiency in addressing real-world challenges, working with diverse datasets, and developing models tailored to specific applications. In the current project, my expertise aligns with the goals outlined—leveraging EfficientNet-V2 and ResNet for Diabetic Retinopathy classification. With a background in machine learning, UI development, and experience handling diverse datasets, I am well-equipped to contribute to the development of accurate and reliable AI-based diagnostic systems, ultimately improving patient outcomes in healthcare technology.
₹7 000 INR 5 päivässä
0,0 (0 arvostelua)
0,0
0,0
Käyttäjän avatar
Comparative Evaluation of EfficientNet-V2 and ResNet for Diabetic Retinopathy Classification We propose a comparative study between EfficientNet-V2 and ResNet for diabetic retinopathy (DR) classification using APTOS 2019 and IDRiD datasets. Leveraging transfer learning and data augmentation, we'll optimize both models for accurate classification across five DR severity levels. Our target audience includes ML experts and healthcare tech enthusiasts. With accessible datasets, we ensure comprehensive model training. Additionally, we'll develop a user-friendly interface for clear result interpretation by healthcare professionals. Ideal candidates possess ML expertise, medical imaging knowledge, UI/UX design skills, and experience with public datasets. Join us in advancing AI-driven medical diagnostics for improved DR detection and patient outcomes. Let's embark on this transformative journey together.
₹9 400 INR 5 päivässä
0,0 (0 arvostelua)
0,0
0,0
Käyttäjän avatar
In your search for a proficient developer and machine learning expert, you've found the perfect fit with me! With my expertise in Java, HTML, CSS, JavaScript, React, Python, C, C++, and SQL alongside a deep understanding of systems architecture, I can design a straightforward and clear user interface to display classification results effectively for healthcare professional key requirement in this project . Furthermore, my track record showcases my proficiency in algorithm design and data structures strong foundation that will come in handy for training the EfficientNet-V2 and ResNet models using publicly available datasets. Complementing this background is my hands-on experience in data mining and machine learning that emphasizes the significance of comprehensive training to attain high accuracy and reliability in disease classification crucial objective of this project. Importantly, my passion for healthcare and technology convergence resonates strongly with this project's goals. Eager to develop impactful solutions that advance the medical field through AI applications like Diabetic Retinopathy diagnosis systems, I am driven by the belief in their potential to improve people lives. In collaboration together, we'll not just contribute meaningful health outcomes but also create groundbreaking advancements in AI-based diagnostics.
₹5 000 INR 3 päivässä
0,0 (0 arvostelua)
0,0
0,0
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As a dedicated student of Artificial Intelligence with a passion for Machine Learning, I strongly resonate with the noble purpose of your project. Not only have I developed a deep understanding and expertise in ML and AI, but in particular, I'm well-versed with both EfficientNet-V2 and ResNet models. I have employed these advanced CNN architectures with transfer learning and data augmentation techniques to derive more robust and reliable models—a crucial aspect in your project's primary goal of accurate DR severity classification. In addition to my technical competence, what makes me an ideal candidate for this task is my profound interest in the intersection of medicine and technology. With an emphasis on medical imaging analysis and my familiarity with Python—a necessity for efficient handling of your large datasets—I am primed to contribute effectively to developing an intuitive user interface specifically tailored for healthcare usage. Miniaturizing complex ML concepts into a neat interface that communicates key insights will aid collaborations between medical practitioners and AI experts.
₹7 000 INR 7 päivässä
0,0 (0 arvostelua)
0,0
0,0
Käyttäjän avatar
Dear Client, I am excited to submit my proposal for this project. With my expertise in deep learning and image classification, coupled with a keen interest in medical imaging applications, I am well-equipped to undertake this study and deliver meaningful insights. Proposed Approach: Data Preprocessing and Augmentation: I will preprocess the retinal images to ensure consistency and quality for analysis. Augmentation techniques such as rotation, scaling, and flipping will be applied to enhance dataset diversity and mitigate overfitting. Model Implementation: EfficientNet-V2 and ResNet architectures will be implemented using popular deep learning frameworks like TensorFlow or PyTorch. Fine-tuning strategies will be employed to adapt pre-trained models to the specific task of DR classification. Training and Validation: The models will be trained using the APTOS 2019 and IDRiD datasets, with appropriate partitioning for training, validation, and testing. Hyperparameters will be tuned using cross-validation to optimize performance and ensure generalization. Performance Evaluation: The trained models will be evaluated based on various performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Statistical analysis will be conducted to determine significant differences in performance between the two architectures. Thank you for considering my proposal. Warm regards, Ramanuj Bhattacharjee
₹8 000 INR 5 päivässä
0,0 (0 arvostelua)
0,0
0,0

Tietoja asiakkaasta

Maan INDIA lippu
New Delhi, India
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Liittynyt syysk. 22, 2023

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