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Top 50 Deep Learning Projects | Deep Learning Projects

DAY 6 DESIGN 1

What is Deep Learning

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.

It is extremely beneficial to data scientists who are tasked with i.e.; collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.

 

Top 50 Deep Learning Projects

  1. Malnutrition Detection using Deep Learning
  2. Banana Leaf Disease Detection using CNN
  3. License Plate Recognition using Fast RCNN and OCR
  4. Quality Testing of Rice grains using Neural Network
  5. Lane and Curve Detection using Deep Learning
  6. Semantic Segmentation using Deep Learning
  7. Fault Classification using deep Learning
  8. Intelligent brain hemorrhage diagnosis using deep learning
  9. Character Recognition using Backpropagation Neural Network
  10. Detecting Diseases in Gastrointestinal Biopsy Images using deep learning
  11. Face verification using Alex net and Deep Learning
  12. Pneumonia detection in X-ray Images using Deep learning
  13. Palmprint recognition using Deep Learning
  14. Person Identification & Classification using LBP & Hog | MATLAB
  15. Glaucoma Detection using CNN and MATLAB
  16. Medical Image Segmentation using Cuckoo Search Optimization I MATLAB
  17. Brain Tumor Analysis Using Cuckoo Search Optimization – MATLAB
  18. Brain Tumor Segmentation using SFCM & CNN | MATLAB
  19. License Plate Recognition using OpenCV | Python
  20. Leaf Disease Detection using OpenCV and Python
  21. Face Counting Application using OpenCV & Python
  22. Food Calories Detection using Deep Learning OpenCV | Python
  23. Pedestrian Detection using MATLAB
  24. Blind Image Blur Estimation Using Neural Network Algorithm – MATLAB
  25. Face Emotion Recognition using CNN | OpenCV and Python
  26. Multiple Object Recognition using OpenCV and Python
  27. Speech Emotion Recognition using Deep Learning | MATLAB
  28. Barcode and QR Code Recognition using OpenCV | Python
  29. Handwritten Digit Classification Using Deep Neural Network In MATLAB
  30. Optical Character Recognition using OpenCV | Python
  31. Logo Detection using Deep Learning OpenCV | Python
  32. Social Distance Monitoring System using OpenCV | Python
  33. Smart Traffic Light Control System based on vehicle count using neural network
  34. Covid -19 Mask Detection using Deep Learning | OpenCV and Python

     

    1. Malnutrition Detection using Deep Learning

    The effective aid is to identify children under the age of five suffering from malnutrition to target with a portion of proper food. It is as physical development and also determines how well malnutrition affects children.

    1. Banana Leaf Disease Detection using CNN

    Banana is affected by numerous diseases and pests. It is rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency.

    1. License Plate Recognition using Fast RCNN and OCR

    The image segmentation technique with RCNN is to detect license plates and read their number using OCR. It is a tested on two datasets that contain images under various conditions poor picture quality, image perspective distortion, bright day, night, and complex environment.

    1. Quality Testing of Rice grains using Neural Network

    A model of quality testing and identification is constructed i.e.; predicated on geometric and color options with the technology of neural network. It is determining the unknown grain varieties and its quality. This technique offers smart leads to an analysis of rice quality.

    1. Lane and Curve Detection using Deep Learning

    A deep learning technique is used to detect the curved path in autonomous vehicles. It is customized lane detection algorithm was implemented to detect the curvature of the lane. Deep learning is used to detect the curved path in autonomous vehicle.

    1. Semantic Segmentation using Deep Learning

    Semantic segmentation is one of the high-level tasks that paves towards complete scene understanding. It is understanding as a core computer vision problem is highlighted by an increasing number of applications nourish from inferring knowledge to imagery.

     

    1. Fault Classification using deep Learning

    It is to create an intelligent model using image processing techniques in order to categorize the internal fault i.e.; which are low, intermediate, medium and high.

    The optimized model is evaluated and validated through analysis of performance indicators frequently used in any classification model. It is captured using infrared thermography camera in which the color image is stored and processed using MATLAB.

     

    1. Intelligent brain hemorrhage diagnosis using deep learning

    This is to investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images the appropriate inputs extracted to an artificial neural network for classification. It is used to verify diagnosis to minimize errors in current methods.

     

    1. Character Recognition using Backpropagation Neural Network

    This system is the base for many different types of applications in various fields, to use in daily lives. It is to recognize the characters using a back propagation algorithm and the effect of variations of error percentage with number of hidden layers in a neural network.

     

    1. Detecting Diseases in Gastrointestinal Biopsy Images using deep learning

    It focuses on methods with applicability to automated diagnosis of images obtained from gastrointestinal biopsies. These deep learning techniques for biopsy images may help detect distinguishing features in tissues affected by enteropathies.

     

    1. Face verification using Alex net and Deep Learning

    The special kind of multi-layer neural networks, designed to recognize visual patterns directly from pixel images with minimal preprocessing.

    This code uses pretrained Alex net deep learning model i.e.; by retraining the model to many users face images to do face recognition and face verification.

    1. Pneumonia detection in X-ray Images using Deep learning

    Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process.

    However, the exception network a more successful result in detecting pneumonia cases.

     

    1. Palmprint recognition using Deep Learning

    Palmprint recognition requires extraction of features before classification which will affect the recognition rate. It uses the convolutional neural network (CNN) structure dense net to realize palmprint recognition.

    The ROI area after processing is taken as input of convolutional neural network.

    1. Person Identification & Classification using LBP & Hog | MATLAB

    This investigates methods and procedures to construct an efficient system to assist blinds in everyday life. It provides the instructions to blinds for efficient navigation and safe guidance by incorporating object in real-time.

    1. Glaucoma Detection using CNN and MATLAB

    It is implemented Glaucoma detection system for medical applications. This system was implemented to detect the various stages of glaucoma.

    The data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. It is proposed algorithm in terms of accuracy.

    1. Medical Image Segmentation using Cuckoo Search Optimization I MATLAB

    The analysis of comparison results illustrated the superior capability of Cuckoo search algorithm in optimizing the enhancement functions for digital image processing.

    1. Brain Tumor Analysis Using Cuckoo Search Optimization – MATLAB

    Cuckoo Search (CS) algorithm is one such algorithm which is efficient in solving optimization problems in varied fields. It discovers how it is efficient in detecting tumors and compare the results with the other used optimization algorithms.

    1. Brain Tumor Segmentation using SFCM & CNN | MATLAB

    Segmentation refers to the process of partitioning a digital image into multiple segments. The segmented primary and secondary regions are compressed with hybrid techniques for telemedicine application.

    It proposes to spot the tumor from MRI scanned medical images using morphological process.

    1. License Plate Recognition using OpenCV | Python

    License Plate Recognition is an image-processing technology used to identify vehicles by license plates. This technology is used in various security and traffic applications.

    The input image is taken and converted into grayscale image and the processed image is filtered through bilateral filter to remove unwanted characters.

    1. Leaf Disease Detection using OpenCV and Python

    The leaf disease diagnosis using image processing techniques for automated vision system used at agricultural field. It is essential one in monitoring large fields of crops, and thus it detects symptoms of disease as soon as it appears on plant leaves.

    1. Face Counting Application using OpenCV & Python

    It is a computer vision technology that helps to visualize human faces in digital images. This technique is a specific use of that deals with detecting instances of semantic objects of a certain class in digital images and videos.

     

    1. Food Calories Detection using Deep Learning OpenCV | Python

    A deep learning algorithm i.e.; used to analyze static food image. By analyzing the composition of the food, the algorithm can calculate how much calories the dish has. It provides a more efficient way of estimating calories.

     

    1. Pedestrian Detection using MATLAB

    Pedestrian detection is an essential and significant task in surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It indicates the presence of pedestrians i.e.; at various scales and locations in the images.

    1. Blind Image Blur Estimation Using Neural Network Algorithm – MATLAB

    It identifies the blur type from a mixed input of image patches corrupted by various blurs with different parameters. The effectiveness of the proposed method in several tasks with better or competitive results compared with the state of the art on two standard image data sets.

    1. Face Emotion Recognition using CNN | OpenCV and Python

    The key elements of face are considered for detection of face and prediction of expressions or emotions of face. To determine the different facial expressions, the variations in each facial feature are used.

    1. Multiple Object Recognition using OpenCV and Python

    The device will be trained to identify test images in order to assess object characteristics. With simulated results show i.e.; used network classifier has a low error rate during higher classification accuracy.

     

    1. Speech Emotion Recognition using Deep Learning | MATLAB

    Speech emotion recognition have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques.

    It enhances the speech interaction and adaption of computer systems according to the mood and emotion of an individual.

    1. Barcode and QR Code Recognition using OpenCV | Python

    It is an open-source computer vision and image processing. Using this library for processing each frame from a video captured by a device. This function takes an image, then identifies the QR code and barcode from the image, and decodes the value of it.

     

    1. Handwritten Digit Classification Using Deep Neural Network In MATLAB

    It is the capability of the computer to identify and understand handwritten digits automatically. The progress in the field of science and technology is being digitalized to reduce human effort.

    It performs Digit Recognition and the analysis of accuracy of algorithms Neural Network.

     

    1. Optical Character Recognition using OpenCV | Python

    Optical Character Recognition is vital and a key aspect and python programming language. The application of such concepts in real-world scenarios is numerous.

    It extracts image segments the detector has identified as containing text and enhance using various image filters from the OpenCV module.

    1. Logo Detection using Deep Learning OpenCV | Python

    Deep learning became a major tool for Ad Tech. A Logo detection system is used to identify the targets from i.e.; brands, products, and logos on publicly posted images. The easiest way to identify brand from images is by its logo.

     

    1. Social Distance Monitoring System using OpenCV | Python

    Social distancing aims to decrease in a population i.e.; by minimizing contact between potentially infected individuals and healthy individuals with high rates of transmission or low levels of transmission.

     

    1. Smart Traffic Light Control System based on vehicle count using neural network

    It develops the traffic control framework by detecting system, which gives an input to the current system, that can adjust the changing traffic density patterns and provides a vital sign to the controller in a continuous activity.

    Using this method, improvement of the traffic signal switching expands i.e.; street limit, saves time, and prevents traffic congestion.

    1. COVID-19 Mask Detection using Deep Learning | OpenCV and Python

    Face Mask detection model is built using the Deep Learning technique. It is built using OpenCV library which is highly used for real-time applications.

    The system uses OpenCV to detect the person with or without a mask. It connected with any surveillance system installed at premises.

     

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