Advanced Deep learning projects for final year students

Are you a final year student and searching for an Advanced Deep Learning Projects? Hope, this article can find you a best way to choose an advanced deep learning projects.

Takeoff Edu Groups furnishes the projects, and covered wide range of topics like computer vision, natural language processing, and generative models. Each topic contain a perfect instructions and resources which helps you to improve your skills and ideas.

Takeoff Edu group- Here are the Latest, Trendy and Standard titles of Advance Deep Learning projects:


Blood Cells Classification Using Deep Learning Technique: There are three major types of blood cells, red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Together, these three kinds of blood cells add up to a total of 45% of the blood tissue by volume, with the remaining 55% of the volume composed of plasma, the liquid component of blood.

Logo Based Amphetamines classification: In this Work, we propose a framework for classifying the top view image of amphetamines based on their logo using SURF and Bag-of-features model. During our experiment, we found that the unsmooth surface of amphetamines and low contrast are the main factors of low accuracy for classification.

Dried Fish Classification Using Deep Learning: Dried fish is a great procedure for fish reservation all over the world. Dried fish is evaluated as a choice food on the menu for a large number of Bangladeshi people.


Recognition of Hand Gestures Using CNN: Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications.

Waste: Video-Based Medical Waste Detection and Classification: Waste auditing is important for effectively reducing the medical waste generated by resource-intensive operating rooms. To replace the current time-intensive and dangerous manual waste auditing method, we propose a system named WASTE to detect and classify medical waste based on videos recorded by a camera-equipped waste container.

Moving Object Detection In Video Streaming Using Improved DNN Algorithm: An efficient approach for MOD (Moving Object Detection) has been implemented in research work. In this proposed work, collect the video samples such as *.avi format. After that, it developed the frame differencing to divide the video into frame format or extraction.


Image Caption Generation using Deep Learning Technique: An Artificial Intelligence (AI), the contents of an image are generated automatically which involves computer vision and NLP (Natural Language Processing). The neural model which is regenerative, is created. It depends on computer vision and machine translation.

Wildfire Segmentation on Satellite Images using Deep Learning: Deep learning and convolutional neural network technologies are increasingly used in the problems of analysis, segmentation and recognition of objects in images. In this article a convolutional neural network for automated wildfire detection on high-resolution aerial photos is presented.

A Deep-Learning Approach to Find Respiratory Syndromes in Infants using Thermal Imaging: Respiratory syndromes being one of the most recurrent issues in a neonate, our methodology involves detection of respiratory rates to identify different types of respiratory syndromes in an infant. Most of the monitoring techniques involve an invasive monitoring approach, which may bring uneasiness to the patient.