nathamsr11 Goto Github PK

automated-gland-segmentation-leading-to-cancer-detection-for-colorectal-biopsy-images

Glandular formation and morphology along with the architectural appearance of glands exhibit significant importance in the detection and prognosis of inflammatory bowel disease and colorectal cancer. The extracted glandular information from segmentation of histopathology images facilitate the pathologists to grade the aggressiveness of tumor. Manual segmentation and classification of glands is often time consuming due to large datasets from a single patient. We are presenting an algorithm that can automate the segmentation as well as classification of H and E (hematoxylin and eosin) stained colorectal cancer histopathology images. In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. Inter as well as intra-gland variability and cellular heterogeneity has made this a strenuous problem. The proposed approach includes intensity-based information, morphological operations along with the Deep Convolutional Neural network (CNN) to evaluate the malignancy of tumor. This method is presented to outpace the traditional algorithms. We used transfer learning technique to train AlexNet for classification. The dataset is taken from MCCAI GlaS challenge which contains total 165 images in which 80 are benign and 85 are malignant. Our algorithm is successful in classification of malignancy with an accuracy of 90.40, Sensitivity 89% and Specificity of 91%. here is a copy of this project from a

blood.vue_firebase

bloodbank

caffeonspark

Distributed deep learning on Hadoop and Spark clusters.

coderbyte-challenges-solutions

CoderByte-Challenges-Solutions

coding_challengeoptions

https://drive.google.com/file/d/15X00ZWBjla7qGOIW33j8865QdF89IyAk/view?usp=sharing\ The dataset is tabular and the features involved should be self-explanatory. We would like for you to come up with a specific problem yourself and solve it properly. This is an “open challenge,” mainly focusing on natural language processing. The problem could be either about predictive modeling or providing analytical insights for some business use cases. Note the problem should be treated as large-scale, as the dataset is large (e.g., >100GB) and will not fit into the RAM of your machine. Python is strongly recommended in terms of the coding language.

coursera_big_data_for_data_engineers

Assignments for Big Data for Data Engineers specialization on Coursera by Yandex.

distributed-system

Distributed System implementation with paxos, consensus algorithm, locking, failure detection, group view and RPCs

django-vue.js-blog

django-vue.js-blog

docker-hadoop-spark-workbench

[EXPERIMENTAL] This repo includes deployment instructions for running HDFS/Spark inside docker containers. Also includes spark-notebook and HDFS FileBrowser.

dp-200-implementing-an-azure-data-solution

e-comm-web-client

epc-advanced-topics

Example programs from class will be posted here.

facial-expression-recognition

Facial expression recognition deep learning examples

flutterchatapptutorial

Fully Functioning Chat App with Flutter & Firebase

fundall

hello-vue-django

vuejs and Django integration with hot code reload

learning-hadoop-and-spark

Companion to Learning Hadoop and Learning Spark courses on Linked In Learning

learntools

Tools and tests used in Kaggle Learn exercises

migrate

Tool to help customers migrate artifacts between Databricks workspaces. This allows customers to export configurations and code artifacts as a backup or as part of a migration between a different workspace.

mmlspark

Microsoft Machine Learning for Apache Spark

node-sqlite3

SQLite3 bindings for Node.js

nodejs.org

The Node.js website.

nplruntime

NPL - Neural Parallel Language

pima-diabetes-prediction-using-tensflow

project-14.-parkinson-s-disease-detection.ipynb

About this file Data Set Information: This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD. Attribute Information: Matrix column entries (attributes): name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency MDVP:Jitter(%) , MDVP:Jitter(Abs) , MDVP:RAP , MDVP:PPQ , Jitter:DDP - Several measures of variation in fundamental frequency MDVP:Shimmer , MDVP:Shimmer(dB) , Shimmer:APQ3 , Shimmer:APQ5 , MDVP:APQ , Shimmer:DDA - Several measures of variation in amplitude NHR , HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) - Parkinson's, (zero) - healthy RPDE , D2 - Two nonlinear dynamical complexity measures DFA - Signal fractal scaling exponent spread1 , spread2 , PPE - Three nonlinear measures of fundamental frequency variation

pwa

PWA template for vue-cli based on the webpack template

pyhive

Python interface to Hive and Presto. 🐝

reat-native

sana

spark

Apache Spark - A unified analytics engine for large-scale data processing