TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture.

Overview

TumorInsight

Overview

What is TumorInsight?

TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture. It aims to detect and classify the brain tumours from MRI scans. The detection is done using Image Processing algorithms and classification using Deep learning techniques.
The model is also deployed as a web application using Flask framework.

Download trained model from here.

Features

  • TumorInsight Web App - Technology at your fingertips!
  • Image Segmentation (K-means clustering)
  • Image Processing (Median filtering, morphological processing etc.)
  • RESNET50 architecture for classification
  • Flask framwework for integrating with frontend of web app
  • And More...

Contents

Project Details

TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture. It aims to detect and classify the brain tumours from MRI scans. The detection is done using Image Processing algorithms and classification using Deep learning techniques.
The model is also deployed as a web application using Flask framework.

In addition, you can also customize the following properties according to the need.

  • Lorem ipsum
  • Lorem ipsum
  • Lorem ipsum

Usage

  1. Lorem Ipsum

Project is designed to be easy to use. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

  1. Lorem Ipsum

Lorem ipsum dolor sit amet, consectetur adipiscing elit.

  1. Lorem Ipsum

Finally, call func(arg) in main()

def func(arg):
    return arg

func(input())

Requirements

  • Xcode 10.2.1
  • Swift 5.0

Installation

Follow these steps to use this project.

  1. Clone the repository.
  2. Lorem ipsum

Contribute

Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/NewFeature)
  3. Commit your Changes (git commit -m 'Add some NewFeature')
  4. Push to the Branch (git push origin feature/NewFeature)
  5. Open a Pull Request

Tech Stacks/Tools Used

License

TumorInsight is available under the MIT license. See the LICENSE file for more info.

Author

Pranav Khurana


Owner
Pranav Khurana
Incoming Software Engineer at UBS | Ex-Research Intern at Arizona State University | 2022 Undergrad
Pranav Khurana
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Google Cloud Platform 792 Dec 28, 2022
Resources related to EMNLP 2021 paper "FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations"

FAME: Feature-based Adversarial Meta-Embeddings This is the companion code for the experiments reported in the paper "FAME: Feature-Based Adversarial

Bosch Research 11 Nov 27, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
Toolbox of models, callbacks, and datasets for AI/ML researchers.

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch Website • Installation • Main

Pytorch Lightning 1.4k Dec 30, 2022
SNIPS: Solving Noisy Inverse Problems Stochastically

SNIPS: Solving Noisy Inverse Problems Stochastically This repo contains the official implementation for the paper SNIPS: Solving Noisy Inverse Problem

Bahjat Kawar 35 Nov 09, 2022
DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021)

DeepLM DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021) Run Please install th

Jingwei Huang 130 Dec 02, 2022
Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline

vqvae_dwt_distiller.pytorch Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline. It allows to generate 512x512 ima

Sergei Belousov 25 Jul 19, 2022
covid question answering datasets and fine tuned models

Covid-QA Fine tuned models for question answering on Covid-19 data. Hosted Inference This model has been contributed to huggingface.Click here to see

Abhijith Neil Abraham 19 Sep 09, 2021
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
Medical Image Segmentation using Squeeze-and-Expansion Transformers

Medical Image Segmentation using Squeeze-and-Expansion Transformers Introduction This repository contains the code of the IJCAI'2021 paper 'Medical Im

askerlee 172 Dec 20, 2022
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks Image Classification Dataset: Google Landmark, COCO, ImageNet Model: Efficient

FedML-AI 62 Dec 10, 2022
Artificial Neural network regression model to predict the energy output in a combined cycle power plant.

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

1 Feb 11, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
This is the workbook I created while I was studying for the Qiskit Associate Developer exam. I hope this becomes useful to others as it was for me :)

A Workbook for the Qiskit Developer Certification Exam Hello everyone! This is Bartu, a fellow Qiskitter. I have recently taken the Certification exam

Bartu Bisgin 66 Dec 10, 2022
Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning

THUML @ Tsinghua University 101 Dec 11, 2022
Multi Agent Path Finding Algorithms

MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

30 Dec 12, 2022
Learning Versatile Neural Architectures by Propagating Network Codes

Learning Versatile Neural Architectures by Propagating Network Codes Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang,

Mingyu Ding 36 Dec 06, 2022
A Next Generation ConvNet by FaceBookResearch Implementation in PyTorch(Original) and TensorFlow.

ConvNeXt A Next Generation ConvNet by FaceBookResearch Implementation in PyTorch(Original) and TensorFlow. A FacebookResearch Implementation on A Conv

Raghvender 2 Feb 14, 2022
object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

赛题背景 在商品知识产权领域,知识产权体现为在线商品的设计和品牌。不幸的是,在每一天,存在着非法商户通过一些对抗手段干扰商标识别来逃避侵权,这带来了很高的知识产权风险和财务损失。为了促进先进的多媒体人工智能技术的发展,以保护企业来之不易的创作和想法免受恶意使用和剽窃,因此提出了鲁棒性标识检测挑战赛

65 Dec 22, 2022