Classification of Lung cancer slide images using deep-learning

This repository provides scripts to reproduce the results in the paper "An annotation-free whole-slide training approach to pathological classification of lung cancer types by deep learning".

This study presents the development and validation of AI models for both nodule detection and cancer classification tasks. This benchmarking across multiple datasets establishes the DLCSD as a reliable resource for lung cancer AI research.

A deep learning-based system for predicting lung cancer from CT scan images using Convolutional Neural Networks (CNN). This project utilizes the Xception model for image classification into four categories: Normal, Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma.

Using machine learning and image processing to classify lung cancer in CT scans

Network Architecture for the ISBI_2018 paper : DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS

Early detecting of lung cancer using the Luna data set with LIDC IDRI annotations using two models nodule classification"Googlent model" and the malignancy classification "Lenet model". This was for kaggle's Data science bowl 2017.

This project focuses on detecting lung cancer using Inception and Resnet50 models with the Chest CTScan dataset. Lung cancer is a leading cause of death worldwide, making early detection critical for successful treatment. Convolutional neural networks, such as Inception and Resnet50, have shown great success in image classification tasks.

The model is based on a YOLOv8 (Deep learning Neural network architecture) and is trained on the publicly available dataset, which consists of lung CT scans of patients with and without lung cancer.

As part of the ICLR 2018 Reproducibility Challenge, built a Convolutional Neural Network (CNN) that classifies lung cancer nodules from the LUNA 2016 CT scan dataset as either benign or cancerous. Achieved accuracy rates of 99.78% with a false positive rates of 0.11%, confirming original paper's performance. (COMP 551 - Machine Learning - Final Project)

Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.

A neural network-based ap proach for optimizing the lung cancer nodule detection and lung cancer stage detection based on pre-processing, segmentation, nodule detection feature extraction and classification of computed tomography scan images.

The primary goal of developing a smart city is to enhance the quality of lives of its citizens by providing infrastructure and offering smart healthcare. Smart Healthcare plays a significant role in achieving this objective of developing smart cities. Here, in this proposal our objective is to develop a smart health care system which uses artificial intelligence for the development of a decision support system in the medical field for the detection and segmentation of lung cancer. The proposed system is consisting of two phases, First phase will be consisting of various stages like Pre-processing, feature extraction, feature selection, classification and finally segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of Crow Search Optimization Algorithm, later Artificial Neural Network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the Fuzzy K-Means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. This methodology delivers a 96% of accuracy, 100% specificity and sensitivity of 99%. The accuracy of the decision taken by the Smart Health Care System exceed when compared to the accuracy of the decision taken by the doctors.

Thoracic Surgery Data Set - UCI Machine Learning. Classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.

Develop a machine learning (ML) model for lung cancer detection using U-Net and DenseNet architectures. Achieve an accuracy of at least 99.96% in lung nodule detection and classification. Achieved validation of 99.9%.

Code to reproduce the results in "Pulmonary nodule classification in lung cancer from 3D thoracic CT scans"

Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion - BMC Bioinformatics

machine learning based lung cancer classification using autofluorescence lifetime microscopic images

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

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Classifying Lung Cancer using a combination of Convolutional Neural Network as features extractor and Random Forest as Classifier

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本系统是一个基于深度学习的肺癌CT图像分类与疾病信息展示平台。系统能够对输入的肺部CT图像进行智能分析，识别可能存在的肺癌类型，并提供相应的医学信息参考。系统集成了图像处理、深度学习分类和医学知识展示等功能，为医疗诊断提供辅助支持。

While classifying the benign and malignant lung cancer nodules, finding out which pixels have a greater impact on the classification

The cancer like lung, prostrate, and colorectal cancers contribute up to 45% of cancer deaths. So it is very important to detect or predict before it reaches to serious stages. If cancer predicted in its early stages, then it helps to save the lives. Statistical methods are generally used for classification of risks of cancer i.e. high risk or low risk. Sometime it becomes difficult to handle the complex interactions of highdimensional data. Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.I am using algorithms like Naive Bayes, decision tree

Classification for microarray data such as breast cancer, leukemia ALL-AML, lung cancer, colon tumor, and ovarian cancer using deep belief network algorithm.

Machine learning tool for analysis of lung adenocarcinoma tumors

Diagnosis of histologic growth patterns of lung cancer in digital slides using deep learning.

kaggle data science bowl 2017 solution

this is matlab code to detect and classify lung cancer from a CT scan