548 skills found · Page 9 of 19
Reshmanazeer07 / Healthcare Management SystemA Console-Based E-HealthCare Management System is a text-interface application that manages patient records, doctor schedules, appointments, billing, and medical history. It streamlines healthcare operations, ensures organized data handling, and is ideal for clinics or educational project use.
priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image ClassificationUsing-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.
upes-open / OSC Medical Analysis Application Using MLAn CNN based binary classification model to classify X-Ray scans on whether they are suffering from pneumonia or not.
DocCheck / RonaRona - the Robotic Nurse Assistant
IoBT-VISTEC / PPMI DLParkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial. The interpretation of the deep learning model to analyze the prediction results of 3D-images data.
TEJASsKoundinya / Medical Insurance Charges Application On Multilinear Regression Flask Deploymenttrying to predict the cost of patient's medical insurance
imi-ms / MoPatMobile Patient Survey (MoPat) is a Java based web application to create, distribute, complete and export medical questionnaires.
bharat-chandra / Voice Prescriptionvoice prescription : A Doctor will be able to dictate his prescription to the patient while talking to his phone or PC running windows. Following key words need to be tagged. eg: name,age, symptoms,medicines. Doctor will say one or more of keywords. • For all keywords data will be captured discreetly. • Doctor will be able to edit / delete by voice or hand any entry that has been made. • Doctor will be able to preview the prescription. • Patient willget the PDF document either directly or as a link in SMS/WhatsApp
radioxoma / HevalMedical calculator for intensive care unit
Ansarimajid / Heart CareThe Heart Care Django Project is a web application developed specifically for heart disease hospitals. It provides a comprehensive platform to efficiently manage patient records, appointments, and medical information. With features like appointment scheduling.
Abhishek-Mallick / Health BuddyHealth Buddy is a web application that provides essential medical assistance, including predicting brain tumors, detecting retina problems, and finding nearby doctors. With a chatbot feature and AI-powered suggestions, it aims to improve the healthcare experience for users.
TiagoFilipeSousaGoncalves / Survey Attention Medical ImagingImplementation of the paper "A survey on attention mechanisms for medical applications: are we moving towards better algorithms?" by Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira and Jaime S. Cardoso.
Shubha76 / Brain Tumor Detection From MRI Images Spring 2018Earlier detection of brain tumors plays a vital role in its treatment as well as dynamically increase the survival rate of the patients. Magnetic Resonance Imaging (MRI) scans are widely used to diagnose the brain tumors which provides better accuracy than other medical imaging techniques. Still, the manual segmentation of MRI images and detecting the brain tumors is a time consuming and prone to error task, which is currently done by the medical experts or radiologists. So, there is an evident necessity for automatic brain tumor segmentation and extracting various characteristics of brain tumors. In this study, three widely used standard image segmentation methods (threshold based, k-means clustering and watershed segmentation) has been tested using collected brain MRI images to isolate the tumors from the rest of the brain regions, and their performance was compared based on the segmentation output. K-means clustering showed a better result than two other methods. Besides this, a graphical user interface (GUI) is designed based on primary image processing techniques and by using the solidity feature of brain tumors. Two of the highly useful brain tumor characteristics (area, and perimeter) are also measured here and displayed on the output window of GUI. The accuracy of this application for tumor detection on brain MRI images and features calculation is much high. More features can be extracted, and the accuracy can be maximized by following some other rigorous techniques, which later could be highly helpful for the medical practitioners working in this field.
gebawe / 3D Shape Prediction From A Single RGB Image With Deep LearningThe goal of this 3D prediction from single RGB image is to predict the 3D geometry and structure of objects from a single RGB image using deep learning techniques. This long standing ill-posed problem is crucial to numerous applications such as robot navigation, object recognition and scene understanding, medical diagnosis, and 3D modeling and animatio n. The advancement of deep learning techniques and the increasing availability of large 3D training data sets, have lead to a new generatio n of 3D shape inference methods that are able to predict the 3D geometry and structure of objects from a single view. In this work, we have experimented on different output representations, i.e. voxel, mesh, and point cloud, and two coordinate systems, commonly known as object-centered and viewer-centered. We have developed an end-to-end learning framework with Variational Autoencoder network, where the recognition network maps both the input image and silhouette, autogenerated using U-Net, to a latent representation and the generative network is expected to perform non-trivial reasoning about the 3D structure of the object, which we tried on chair category of ShapeNet dataset and achieved a comparable result to the state of the art
pedromelobitencourt / MedicalClinicWebApplication ProjectOur Medical Clinic Web App offers a two-fold experience: public access for easy appointment booking and clinic info, and exclusive staff access to manage records and appointments
gtancev / Medical Language Model LearnerThis application guides you through the development of a language model that classifies clinical documents according to their medical speciality.
gabbygab1233 / Medical Symptoms ClassifierAI application that can classify medical text according to the category of the ailment being described.
PapithaDharmalingam / Healthcare DAPPThe main purpose of this application is to develop a system based on blockchain technology for a patient centric medical record management and for storing hospital data relating to drugs and medical equipment
SaiHitesh16 / Glaucoma Detection Using CNNGlaucoma detection using deep learning(cnn)
genixtec / Android Dicom Image ProcessingAndroid - DICOM image processing using Imebra
