Cheng Lei's starred repositories
awesome-selfhosted
A list of Free Software network services and web applications which can be hosted on your own servers
github-readme-stats
:zap: Dynamically generated stats for your github readmes
gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
cs-self-learning
计算机自学指南
paper-reading
深度学习经典、新论文逐段精读
SwitchHosts
Switch hosts quickly!
gpt4-pdf-chatbot-langchain
GPT4 & LangChain Chatbot for large PDF docs
CVPR2024-Paper-Code-Interpretation
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
best_AI_papers_2022
A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.
awesome-ml4co
Awesome machine learning for combinatorial optimization papers.
EssayTopicPredictV2
高考作文题目预测模型 v1.0
gpt4-with-calc
GPT-4 Equipped with Numeric Calculation
neuralBlack
A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework
Lecture-Video-to-PDF
Making lecture videos readable
Brain-Tumor-MRI-Classification
Brain Tomur Classification Using Pre-trained Models
ecnu-master-thesis-template
2022华东师范大学硕士论文模版
Algorithms_Note
算法工程师技术栈学习笔记
Brain_Tumor_Classification
Brain Tumor Classification using Transfer Learning
Classification-of-Brain-tumor-Using-CNN-in-Deep-Learning-approach
This project is being done as a part of 1nd semester ME curriculum. The major objective of this project is to detect if the brain tumour is present or not by training the machine using pre-processed Magnetic resonance imaging (MRI) using Deep Learning. The segmentation, detection, and extraction of infected tumor area from magnetic resonance(MR) scan.We estimate the brain tumor severity using Convolutional Neural Network algorithm which gives us accurate results.
tumor-classification
Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success