Pneumonia Classification Using Deep Learning Expertise paper writing
Budget: $30 – $250 USD
job Posting: Deep Learning Expert for Pneumonia Classification
We are seeking an experienced deep learning professional to develop an ensemble model that combines Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the classification of pneumonia from chest X-ray images. The goal is to accurately distinguish between healthy and diseased cases.
Key Responsibilities
Design and implement a CNN model and integrate it with ViT for ensemble learning.
Train and fine-tune the models for optimal accuracy and robustness.
Deliver well-documented code and a detailed implementation report.
write a research paper to be published in a journal
Requirements
Proven expertise in CNNs, image classification, and deep learning.
Proficiency in Python and frameworks such as TensorFlow, Keras, or PyTorch.
Strong understanding of image preprocessing techniques (normalization, augmentation, resizing, etc.).
Prior publication experience in machine learning or healthcare-related research is required.
Deliverables
A trained and validated CNN–ViT ensemble model.
Complete, well-structured code with documentation.
Comprehensive performance metrics and evaluation report.
Note : payment will be released after the journal approval
We are seeking an experienced deep learning professional to develop an ensemble model that combines Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the classification of pneumonia from chest X-ray images. The goal is to accurately distinguish between healthy and diseased cases.
Key Responsibilities
Design and implement a CNN model and integrate it with ViT for ensemble learning.
Train and fine-tune the models for optimal accuracy and robustness.
Deliver well-documented code and a detailed implementation report.
write a research paper to be published in a journal
Requirements
Proven expertise in CNNs, image classification, and deep learning.
Proficiency in Python and frameworks such as TensorFlow, Keras, or PyTorch.
Strong understanding of image preprocessing techniques (normalization, augmentation, resizing, etc.).
Prior publication experience in machine learning or healthcare-related research is required.
Deliverables
A trained and validated CNN–ViT ensemble model.
Complete, well-structured code with documentation.
Comprehensive performance metrics and evaluation report.
Note : payment will be released after the journal approval