Undergraduate Projects
2016 - 2017
Advisor: Tokunbo Ogunfunmi
Students: Natalie Arrizon, Jose Hernandez, Antonio Maldonado-Liu, Alejandra Pacheco
In many developing countries, a large percentage of the population lacks access to adequate healthcare. This is especially true in India where close to 70% of the population live in rural areas and have little to no access to hospitals or clinics. People living in rural India often times cannot afford to pay to see a doctor should they need to make their visits to a hospital.
Telemedicine, a breakthrough in the past couple decades, has broken down the barrier between the patient and the physician. It has slowly been implemented in India to make doctors more available to patients through the use of video conferences and other forms of communication. To improve the outcome of virtual visits via telemedicine, we have developed a compact and affordable kit that will be used to take a patient’s blood pressure, heart rate, blood glucose concentration and oxygen saturation. In addition to our sensor development, by wirelessly sending data results from the vital sign kit, the first essential part of a treatment can be carried out via wireless communication, saving the doctor and patient time and money.
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2017 - 2018
Advisor: Tokunbo Ogunfunmi
Students: Juliana Shihadeh, Anaam Ansari
This research applies Convolutional Neural Networks to advance access to medical diagnosis. Here, we focus on classifying skin cancer images as Benign or Malignant as a starting base to build on to develop an app that allows easily-accessible medical diagnosis in underdeveloped
We hope to expand our work to be able to classify additional physically visible diseases in addition to Skin Cancer since a camera only captures exterior physically visible diseases. All our training and testing presented in this paper have been run on the NVIDIA Jetson TX2 GPU. Results are promising, showing accuracy rates of up to 74 percent depending on how neural network parameters are changed. We intend to incorporate this technology with a previously developed Vital Signs Multi-sensor kit [1]. The kit will be a compact and affordable device equipped with sensors that can be used to take a patient's vital signs, such as blood pressure, heart rate, blood glucose concentration, and blood oxygen saturation. Combined, the tools will provide a complete system for remote medical diagnosis.
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2021 - 2022
Advisor: Tokunbo Ogunfunmi
Students: Ivy Chung and Anoushka Gupta
This project applies deep learning and Internet of Things technologies to aid in agriculture. Agriculture is such a vital part of our society, and according to the United Nations’ Food and Agricultural Organization (FAO), plant diseases are considered one of the two main causes of decreasing food availability.
This project explores not only the methods and findings of building a CNN disease detection model, but that of building a deployable remote crop disease detection system incorporating IoT technology.
By using transfer learning with AlexNet, we were able to predict with 89.8% accuracy tomato plant images into one of the ten pre-defined disease classes. Our proposed system tracks plant health throughout the day by using a microprocessor and a camera to automatically capture images, diagnose the plant, and report results. The system is a proof of concept of a technology that can significantly help increase crop yield, reduce food waste, and automate the tasks of detecting and caring for diseased crops.
View the publication here!