Lung Cancer Classifier with ViT

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards.

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@Osama141

Hey! I've worked on a number of projects similar to this one. I have a lot of experience and knowledge in this field. My knowledge of business also gives me an advantage in this situation. Looking forward for the opportunity. Thanks

@Ahmed8033

Hello, I have masters degree in Robotics and have a very good understanding of computer vision. I have developed many Deep Learning based models for medical applications. I am confident to develop a ViT based deep learning model for lungs cancer classification. I can also design API and deploy the system. Feel free to inbox me so I can share the previous work smaple related to your project.

@sant527

I work in python mainly. I also work with javascript/reactjs for frontend I have good knowledge of database, cloud deployment and docker containarization of apps. I worked with creating chat bots using chatgpt using rag pipeline I would like to work on this project. Lets discuss more on this project

@Web1devloper

With a robust eight years of experience in data analytics and machine learning, I am confident I can meet every aspect of your project's requirements. My deep understanding of Python, particularly the libraries required for your Deep Learning architecture like NumPy, Pandas, and Matplotlib combined with my skills with Tensorflow and PyTorch make me an ideal candidate for developing and fine-tuning the Vision Transformer (ViT) model. Furthermore, my familiarity with Docker ensures that the final product will be easily containerized and ready for deployment. I've developed numerous applications using Streamlit, and FastAPI - the very frameworks you prefer for this project. My proficiency in these frameworks will aid in building a highly efficient **CT scan classifier** and ensure a user-friendly, detailed result display. Contrary to just being able to build such models, I believe it is essential to translate complex insights from them into meaningful information which I can provide through my data storytelling skills. Additionally, I appreciate the significance of precision, recall metrics in clinical applications and my extensive experience plus knowledge of maintaining model performance can guarantee we get nothing short of accurate results. Moreover, having worked in various verticals including healthcare lends me some much-needed domain expertise for precisely handling lung CT scans.

@vishals397

Hello, As the founder of CentoCode Technologies, my team and I specialize in delivering dynamic IT solutions that not only drive operational efficiency but also ensure transformative growth. Regarding your project's requirements, my team's profound expertise in Deep Learning and Machine Learning, particularly with the Vision Transformers (ViT), uniquely positions us to tackle your lung cancer classification system head-on. The fine-tuning of ViT models and building robust training pipelines for model development is a routine activity at CentoCode; thus, we guarantee a solid foundation for the system you seek. The significance of precise communication in medical imaging is something I understand deeply, having developed CRMs for Visionway Immigration among other successful projects. I assure you, the Streamlit GUI where detailed result displays, uploads, and CT scan classifications will be managed is within our wheelhouse. My background in not just Django but also FastAPI will indubitably ensure that your desired backend with API deployment sees seamless execution. Thanks

@corextech

Hi there! I can deliver your end-to-end Lung Cancer Classification System in a realistic timeframe of just two weeks. I will be fine tuning the ViT (Vision Transformer) on the dataset of CT Scan images. The Streamlit UI will ensure smooth user experience of getting detailed analysis of uploaded image with class name (Normal, Benign or Malignant) and relative details along attention maps based explanation. You will receive modular python code in Jupyter Notebooks with proper Docker containerization ability. I have previously worked on a Lung Cancer prediction model which used symptoms dataset along with regression models for prediction/classification for patients. Similarly, I have built and deployed multiple projects using Streamlit such as RAG based chatbot for clinical diagnosis of the patients. Lets connect and discuss. Regards, AI (Asif Iqbal, not Artificial Intelligence)

@HASEEB1803

Hello, I am excited about your project on lung cancer classification using Vision Transformers (ViT). I have hands-on experience in deep learning for medical imaging, including a similar project where I built a classifier to detect pneumonia from chest X-ray scans. That experience gives me a solid foundation in handling medical datasets, ensuring high precision and recall, and designing models that are robust for clinical use. Why me? +Strong background in deep learning, computer vision, and model explainability +Experience with Streamlit + FastAPI + Docker for end-to-end deployment +Proven track record in medical imaging classification (pneumonia detection project) +Commitment to delivering modular, production-ready Python code optimized for maintainability I would love to bring the same rigor and accuracy from my past work into your project, ensuring a reliable, explainable, and deployable system. Looking forward to collaborating. Best regards, Muhammad Haseeb

@IsaacSmith77

Hi, I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided!

@Wbnestmedia

As an experienced deep learning engineer at WebNest Media, I am skillful in tackling complex projects just like yours. With a strong background in Python and in-depth knowledge of deep learning, particularly Vision Transformer (ViT), I'm confident in my ability to develop your Lung Cancer Classifier with precision and recall being the key metrics. Throughout my 8 years of experience, I have developed a knack for building clean, maintainable, and modular code - a skill I will bring into your project to ensure it is ready for clinical deployment. What sets me apart lies in my proficiency not just with ViT, but also with the tools integral to your project such as Streamlit and FastAPI. Additionally, I understand the significance of explainability in medical applications and excel at implementing it. I can deliver an intuitive Streamlit GUI that focuses on detailed result display and allows efficient upload and classification of CT scans. Moreover, given our proficiency in Docker, we'll optimize your VI model's deployment using FastAPI backend to ensure utmost efficiency. My vast experience goes beyond coding: it includes not only technical expertise but also a strong problem-solving mindset - an invaluable asset when working on custom projects like yours. So let's collaborate to turn your vision into reality and make a tangible difference in lung cancer prognosis!

@shailender18

Hi I am expert as per your job lest start chat With an impressive track record in full-stack development, I'm uniquely qualified to tackle your lung cancer classifier project. My experience spans a wide range of technology and design aspects, including proficiency in deep learning, FastAPI, and Streamlit - all essential for your project. Moreover, I have a deep understanding of the precision and recall metrics which will allow me to fine-tune the Vision Transformer model for even more optimal results. In terms of code organization, maintainability is my topmost priority. I develop modular code structures that not only lend themselves well to understanding and collaboration but also make future updates seamless. This emphasis on cleanliness extends to other core technologies as well, such as Docker support for convenient containerization. Moreover, as someone with an extensive background in medical imaging, I understand how crucial efficient and detailed result display is for this domain. In addition to leveraging ViT's explainability features like attention maps, I will ensure that your Streamlit GUI provides thorough information and enables easy uploading and classification of CT scans. By choosing me for this project, you'll gain from my diverse skill set as well as from mycommitment to building high-performing digital solutions tailored to specific needs. Let's discuss your vision further so we can begin making it into a reality together.

@wianleroux

"I am a perfect fit for your project, with a focus on clean, professional, and user-friendly development. While I am new to freelancer.com, I have tons of experience and have done other projects off site. I specialize in deep learning, ViT models, Streamlit, FastAPI, Docker, precision, recall metrics, and medical imaging. I will ensure modular Python code and a seamless system for clinical deployment. I would love to chat more about your project! Regards, Wian"

@dylanc88

"Your idea is perfect for my expertise! I am well-versed in deep learning with Vision Transformers. I can develop a seamless lung cancer classification system using ViT, focusing on precision and recall metrics. While new here, off-site experience has equipped me with the skills needed. I offer proficiency in ViT, Streamlit, FastAPI, Docker, and Python. I would love to chat more about your project! Regards, Dylan"

@webskittersIT

Webskitters LLC is a national award-winning company recognized for its web development, design, and digital marketing excellence. With 3,000+ successful projects and 7+ years of expertise, Webskitters LLC transforms your vision into high-impact digital reality. Trusted by global brands, built for results. Hi, I understand you need a lung cancer classification system using Vision Transformers (ViT) to categorize CT scans into Normal, Benign, or Malignant, with a user-friendly GUI and API deployment. The goal is a high-performance, clinically-ready system with explainable results. My approach: I will fine-tune a ViT model and build a robust training pipeline, integrating FastAPI for backend deployment and Streamlit for an interactive GUI. Users will be able to upload CT scans, view detailed results with attention-based explainability maps, and get precise classifications. The system will be modular, containerized with Docker, and optimized for maintainability and clinical-grade performance. Tech stack: Python & PyTorch for deep learning and ViT implementation Streamlit for GUI development FastAPI for API endpoints and deployment Docker for containerization Modular Python architecture for maintainability Attention maps for explainable AI I’m ready to start immediately—let’s connect so I can deliver a reliable, accurate, and explainable lung cancer classification system efficiently. Best regards, Atanu.S

@NawaazJasat

We are a perfect match - Your search is over. I have extensive experience in deep learning and ViT models, with expertise in building Streamlit apps and FastAPI, along with a strong grasp of precision and recall metrics. I recently completed a similar project involving lung nodule classification, delivering a clean, professional, and seamless solution. To bring your vision to life: 1. Develop a fine-tuned ViT model for lung cancer classification 2. Create a Streamlit GUI focusing on detailed result display and CT scan classification 3. Implement a FastAPI backend for API deployment 4. Enhance explainability using ViT attention maps 5. Support Docker for easy containerization 6. Ensure modular Python code for easy maintainability My track record of punctuality, attention to detail, and effective communication make me the perfect fit for this project. Let's Chat - I'm ready to get started!

@bsvpn

Hi, how are you doing? I’m a deep learning engineer with hands-on work in ViT models and medical imaging pipelines, and I can deliver a fine-tuned ViT for CT scan classification (Normal/Benign/Malignant) with a robust training workflow, a Streamlit GUI for detailed results, a FastAPI backend for deployment, explainability via attention maps, and Dockerized, modular code ready for clinical use. I’ll focus on precision and recall, provide clear evaluation metrics, and ensure clean separation of model, UI, and API components for maintainability.

@iampriyankat

I am a PhD student and a data scientist with 8 years in IT and 4 years’ hands-on experience building machine learning models in Python. I hold a Master’s in Business Analytics and specialize in supervised learning, including binary and multiclass classification using scikit-learn, XGBoost, and ensemble methods. I will work on this dataset, clean, preprocess, and analyze your dataset, build an optimized classifier, and deliver detailed documentation, all within your 7-day timeline. I am confident I can meet your requirements efficiently and professionally. Thank you for the opportunity.

@omkaargrp

With a wide range of expertise across diverse fields, my team and I are the ideal candidates to take on this challenging project on lung cancer classification. Deeply passionate about computing technology and research, we bring an unwavering dedication to innovation in developing informative, practical and robust solutions that cater to the demands of a global audience. While modernizing existing systems or creating something entirely new, we ensure that our projects are ready for real-world use. Our proven track record in Software Development, Web Development and Research Work would seamlessly integrate with your need for a Lung Cancer Classifier with ViT. The meticulous precision necessary for working in medical imaging is second nature to us, and we have the know-how of effectively utilizing precision and recall metrics. Therefore, if you choose us for your project, you'll get not just an efficient Lung Cancer Classifier, but also clear attention maps through ViT explaining the outcomes - an asset to clinicians who require transparency alongside accuracy in such applications. Let’s turn your vision into reality!

@faisaltitu

Hello, I am an experienced Deep Learning Engineer with strong expertise in Vision Transformers (ViT), medical imaging, and model deployment. I propose to build your Lung Cancer Classification System (Normal / Benign / Malignant) with the following workflow: Project Plan: 1. Data Preparation & Preprocessing – clean and normalize CT scans for training. 2. Model Development – fine-tune ViT for high precision & recall. 3. Explainability – integrate ViT attention maps for clinical interpretability. 4. Interface & API – Streamlit GUI for scan upload & results, FastAPI backend for deployment. 5. Containerization – Dockerized solution for portability and clinical deployment. Why Me?: Relevant projects: ViT-SRGAN based Enhanced Surveillance System, 3D Medical Image Synthesis with Diffusion Models, CLIP-based Image Retrieval System – all showcase my experience in transformers, medical imaging, and multimodal AI, directly aligning with your requirements. Skilled in Streamlit, FastAPI, Docker, ensuring a modular and production-ready pipeline. Focused on precision & recall to meet clinical reliability standards. I can deliver a robust, explainable, and deployable system within your timeline and budget. Best regards, Faisal