This blog post provides Summary of to 25 Deep learning projects using matlab and python

Leaf Disease detection using Alexnet -Matlab

This project uses Transfer learning concept from deep learning.Leaf disease is detected and Classified based on Deep Learning. Implementation was done in Matlab using deep learning toolbox.

Lane detection and Assistance system using CNN- Matlab

In recent years, the automatic driving technology has developed rapidly. The detection of the lane line is one of the important contents. This project sorts out the recent lane detection algorithm and the deep learning network, and analyzes the network RCNN based on the segmentation to detect the lane line, and using the object detection-based algorithm RFCN for experimental comparison. According to the analysis of the experimental results, the algorithm based on object detection has advantages in object localization, but it still needs to use post-processing to fit a straight line. 

Lung Cancer detection using CNN-Matlab

In thsi project detection is done using deeplearning matlab

Glaucoma detection using CNN,Densenet-matlab

Glaucoma progressively affects the optic nerve and may cause partial or complete vision loss. Raised intramuscular pressure is the only factor which can be modified to prevent blindness from this condition. Accurate early detection and continuous screening may prevent the vision loss. Computer aided diagnosis (CAD) is a non-invasive technique which can detect the glaucoma in its early stage using digital fundus images. Developing
such a system require diverse huge database in order to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of glaucoma using deep learning technique.

Diabetic Retinopathy using Transfer Learning-Matlab

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

Face recognition using Deep learning-Matlab

With the advent of the era of big data, deep learning theory has been rapidly developed and applied, especially in the field of image recognition. Compared with the classic recognition algorithm such as LBP  and PCA  algorithm, deep learning algorithm has the characteristics of high recognition rate and strong robustness. Based on the principle of convolution neural network (CNN), a realtime face recognition method on Matlab was proposed, which improves the speed and accuracy of face recognition.

Hand Gesture recognition using Deep Learning-Matlab

Face Emotion Recognition using Python

Food classification using Deep learning

Yolo based object detection using Python

Breast Cancer detection using RCNN

Banana ripening stage detection using Deep Learning

Banana Leaf disease detection using deep learning

Banana leaf diseases are important factors as they result in serious reduction in quality and quantity of agriculture products. Therefore, early detection and diagnosis of these diseases are important. To this end, we propose a deep learning-based approach that automates the process of classifying banana leaves diseases. In particular, we make use of the  convolutional neural network to classify image data sets. The preliminary results demonstrate the effectiveness of the proposed approach even under challenging conditions such as illumination, complex background, different resolution, size, pose, and orientation of real scene images.

Malnutrition identification using deep learning