Introduction of a new Dataset and Method for Detecting and Counting the Pistachios based on Deep Learning

Pistachio is a nutritious nut that has many uses in the food industry. Iran is one of its largest producers, and pistachio is considered as a strategic export product for this country. Pistachios are sorted based on the shape of their shell into two categories: Open-mouth and Closed-mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. In the countries that are famous in pistachio production and exporting, there are companies that pack the picked pistachios from the trees and make them ready for exporting. As there are differences between the price and the demand of the open-mouth and closed-mouth pistachios, it is considerable for these companies to know precisely how much of these two kinds of pistachios exist in each packed package. In this paper, we have introduced and shared a new dataset of pistachios, which is called Pesteh-Set. Pesteh-Set includes 6 videos with a total length of 164 seconds and 561 moving pistachios. It also contains 423 labeled images that totally include 3927 labeled pistachios. At the first stage, we have used RetinaNet, the deep fully convolutional object detector for detecting the pistachios in the video frames. In the second stage, we introduce our method for counting the open-mouth and closed-mouth pistachios in the videos. Pistachios that move and roll on the transportation line may appear as closed-mouth in some frames and as open-mouth in other frames. With this circumstance, the main challenge of our work is to count these two kinds of pistachios correctly and fast. Our introduced method performs very fast with no need for GPU, and it also achieves good counting results. The computed accuracy of our counting method is 94.75%. Our proposed methods can be remotely performed by using the videos taken from the implemented cameras that could monitor the pistachios.

A New Modified Deep Convolutional Neural Network for Detecting COVID-19 from X-ray Images

COVID-19 has become a serious health problem all around the world. It is confirmed that this virus has taken over 126,607 lives until today. Since the beginning of its spreading, many Artificial Intelligence researchers developed systems and methods for predicting the virus's behavior or detecting the infection. One of the possible ways of determining the patient infection to COVID-19 is through analyzing the chest X-ray images. As there are a large number of patients in hospitals, it would be time-consuming and difficult to examine lots of X-ray images, so it can be very useful to develop an AI network that does this job automatically. In this paper, we have trained several deep convolutional networks with the introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Unfortunately, most of the previous works on this subject have not shared their dataset, and we had to deal with few data on covid19 cases. Our data contains 180 X-ray images that belong to persons infected to COVID-19, so we tried to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have few cases of COVID-19, and also we propose a neural network that is a concatenation of Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. In this paper, despite some other researches, we have tested our network on 11302 images to report the actual accuracy our network can achieve in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.56%, and the overall average accuracy for all classes is 91.4%.

Sperm Detection and Tracking in Phase-Contrast Microscopy Image Sequences using Deep Learning and Modified CSR-DCF

Nowadays, computer-aided sperm analysis (CASA) systems have made a big leap in extracting the characteristics of spermatozoa for studies or measuring human fertility. The first step in sperm characteristics analysis is sperm detection in the frames of the video sample. In this article, we used a deep fully convolutional network, as the object detector. Sperms are small objects with few attributes, that makes the detection more difficult in high-density samples and especially when there are other particles in semen, which could be like sperm heads. One of the main attributes of sperms is their movement, but this attribute cannot be extracted when only one frame would be fed to the network. To improve the performance of the sperm detection network, we concatenated some consecutive frames to use as the input of the network. With this method, the motility attribute has also been extracted, and then with the help of deep convolutional layers, we have achieved high accuracy in sperm detection. In the tracking phase, we modify the CSR-DCF algorithm. This method also has shown excellent results in sperm tracking even in high-density sperm samples, occlusions, sperm colliding, and when sperms exit from a frame and re-enter in the next frames. The average precision of the detection phase is 99.1%, and the F1 score of the tracking method evaluation is 96.46%. These results can be a great help in studies investigating sperm behavior and analyzing fertility possibility.