Pinned Repositories
AI4M
AI for Medicine Specialization on Coursera. (https://www.coursera.org/learn/ai-for-medical-diagnosis)
awesome-health-datasets
Open datasets in Healthcare
datascience-pizza
🍕 Repositório para juntar informações sobre materiais de estudo em análise de dados e áreas afins, empresas que trabalham com dados e dicionário de conceitos
datasus-datapackage
healthdata-machine-learning
Studies roadmap for Healthdata e Machine Learning
icdatascience
study repo of my scientific initiation research with data science in healthcare
medipynb
ipython notebooks about medicine
MLS
Machine Learning in Healthcare Course with R
portfolio
Projetos e desafios
PythonFundamentos
Repositório do Curso Online Python Fundamentos
fabianofilho's Repositories
fabianofilho/emescam_geolocation
Emescam geolocation diseases
fabianofilho/vertical-medical
Open Source Healthcare System for Odoo
fabianofilho/awesome-healthcare
Curated list of awesome open source healthcare software, libraries, tools and resources.
fabianofilho/blockchain-python-tutorial
Source Code for my blog post: A Practical Introduction to Blockchain with Python
fabianofilho/Telegram-bot
A simple nodejs telegram bot
fabianofilho/Medical-Image-Classification-using-deep-learning
Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
fabianofilho/bluehack-2017
Blue Hack
fabianofilho/teachable-machine
Explore how machine learning works, live in the browser. No coding required.
fabianofilho/health-dome
Health dome project
fabianofilho/free-data-science-books
Free resources for learning data science