This project is our submission, for the Google Solution Challenge 2023. With this project, we hope to make an impact and contribute to the field of Good Health and Wellbeing. This project aims to make early stage cancer detection of various types (specifically Brain Tumor, Breast Cancer & Leukemia) sustainable.

Eye Disease Detection using Transfer Learning (DenseNet-121, EfficientNetB3, VGG-16, Resnet-152)

the advancement of Alzheimer's illness utilizing Convolution Neural Systems (CNN) and EfficientNetB3 architecture, which was applied to pre-processed MRI datasets. The purpose of the project is to use efficient high-performance computing (HPC) to improve the performance of the model, which makes the diagnostic process more efficient and reliable th

We have proposed a multimodal approach. Where we first took the best unimodal for textual and visual data classification by testing and automation process. Then we fusion of the two models which can successfully classify the materials that have been damaged using the image and text data. EfficientNetB3+BERT multimodal better accuracy with 94.18%

2024-2 '딥러닝기반데이터분석' 강의 과제: CNN Fine-Tuning | EfficientNet-B3를 파인튜닝하여 Chest X-Ray로 폐렴과 코로나를 분류하는 Project 진행 | 백엔드: FastAPI, 프론트엔드: Streamlit

Driver Distraction Detection with CNN and Transfer Learning (VGG19, EfficientNet)

Skin diseases can be detected and classified through Deep Learning techniques. In this project Deep CNN network is built on top of EfficientNetB3 for image classification.

To preform image classification on Rice Leaf Disease images using CNN via various methods.

Efficient Brain Tumor Classificataion using Filter-Based Deep Feature Selection Methodology