Image Classification Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Image Classification ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Image Classification Projects
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Efficient Quantum Image Classification Using Single Qubit Encoding
This project explores using quantum computing to classify images more efficiently. It develops a new method that uses only a single quantum bit to mimic traditional deep learning techniques. The approach reduces complexity and requires fewer resources than existing methods. Tests on common image datasets show promising accuracy, and the method could be improved further in the future. -
Masked Modeling-Based Ultrasound Image Classification via SelfSupervised Learning
This project uses artificial intelligence to improve how ultrasound images are analyzed. It teaches a computer to understand images without needing human labeling. The system learns by filling in missing parts of images, helping it recognize patterns more effectively. This method makes ultrasound image classification more accurate, even when the images are unclear or noisy. -
UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project uses artificial intelligence to analyze medical images. It can learn from a small number of labeled images and many unlabeled ones. The system extracts important features from unlabeled data and improves its accuracy with labeled data. It achieves very high accuracy even with only half the labeled data. -
Center-Focused Affinity Loss for Class Imbalance Histology Image Classification
This project focuses on helping doctors detect cancer early by analyzing medical images of tissues. The researchers created a new computer method that can better identify cancer cells, even when the data is uneven or hard to read. Their approach improves accuracy compared to existing techniques and works well on breast and colon cancer images. It aims to make cancer diagnosis faster and more reliable. -
UKSSL Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
This project develops a deep learning system to analyze medical images when only a few labeled examples are available. It first learns useful features from many unlabeled images and then fine-tunes the model using the limited labeled data. The method works well on standard medical image datasets and achieves high accuracy, even better than some models trained with fully labeled data. This approach helps make medical image analysis more efficient when labeling is costly or slow. -
An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis using Deep Ensemble Strategy
This project develops an AI system to detect Covid-19 and pneumonia from chest X-ray images. It uses advanced deep learning models to automatically learn important features from the images. The system combines multiple models to improve accuracy and reliability. Tests show it can diagnose diseases quickly and more accurately than traditional methods. -
An Improved Densenet Deep Neural Network Model for Tuberculosis Detection Using Chest X-Ray Images
This project focuses on detecting tuberculosis from chest X-ray images using a new deep learning model called CBAMWDnet. The model learns important features from the images to identify TB accurately. Tests show it performs better than existing methods, achieving high accuracy and reliability. It can help doctors diagnose TB early and reduce the disease’s spread. -
Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction
This project improves federated learning for devices with different computing powers and network conditions. It uses a new framework called AMA-FES to make training more stable and accurate. Low-power devices only update part of the model to save computation. The system is tested with drones doing image classification and shows better results without extra cost. -
A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
This project compares traditional image recognition methods with a new approach using quantum computing. It builds a Quantum Convolutional Neural Network to process images faster and more accurately than normal CNNs. The model was tested on image datasets like MNIST and COVIDX-CXR3. The results show that the quantum model improves both speed and accuracy in image classification. -
A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages
This project focuses on identifying the different growth stages of pomegranates using images. It uses machine learning to recognize stages like bud, flower, and ripe fruit. The model helps farmers know the exact stage of their crops early. This can improve yield, quality, and reduce losses from pests or diseases.
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