A machine learning-based system for detecting leishmaniasis in microscopic images

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Fractional q -Integral Operators for the Product of a q -Polynomial and q -Analogue of the I -Functions and Their Applications

In this article, we derive four theorems concerning the fractional integral image for the product of the q -analogue of general class of polynomials with the q -analogue of the I -functions. To illustrate our main results, we use q -fractional integrals of Erdélyi–Kober type and generalized Weyl type fractional operators. The study concludes with a variety of results that can be obtained by using the relationship between the Erdélyi–Kober type and the Riemann–Liouville q -fractional integrals, as well as the relationship between the generalized Weyl type and the Weyl type q -fractional integrals.

Segmentasi Citra Wajah dengan Implementasi Adaptif Threshold- Integral Image

<p>Segmentasi wajah merupakan bagian penting dalam pengolahan citra digital untuk mengetahui objek wajah dalam citra sebelum dilakukan pendeteksian ekspresi wajah. Adaptif <em>Threshold – Integral Image</em> adalah salah satu teknik segmentasi berbasis <em>pixel-based</em>,<em> </em>yaitu <em>local thresholding</em>. Penelitian ini bertujuan untuk memisahkan objek wajah manusia dan <em>background </em>-nya. Citra wajah yang akan digunakan nanti citra di dalam ruangan (<em>indoor</em>)<em> </em>dan di luar ruangan (<em>outdoor</em>) dengan resolusi gambar 300x400 piksel. Pada penelitian ini juga mencari nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) yang terbaik dengan melakukan 16 kali percobaan. Dan didapatkan hasil terbaik, yaitu citra di dalam ruangan (<em>indoor</em>) nilai S=1/2 dan T=50, serta citra di luar ruangan (<em>outdoor</em>) nilai S=1/30 dan T=30. Segmentasi citra wajah dengan menggunakan metode Adaptif <em>Threshold – Integral Image</em> <em>robust</em> (kuat) terhadap intensitas cahaya tinggi dan rendah dengan mengatur nilai parameter S (<em>kernel</em>) dan T (<em>Threshold</em>) maka metode ini mampu memisahkan objek wajah dan <em>background</em> -nya. Dari hasil uji coba <em>threshold</em> menggunakan metode Adaptif <em>Threshold – Integral Image</em> terhadap citra di dalam ruangan (<em>indoor)</em> dan di luar ruangan (<em>outdoor)</em> menghasilkan <em>thresholding</em> yang baik dengan mempertimbangkan nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) memberikan hasil dengan tingkat akurasi yang tinggi, yaitu citra di dalam ruangan (<em>indoor</em>) sebesar 96.72%, dan citra di luar ruangan (<em>outdoor</em>) sebesar 93.59%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Face segmentation is an important in digital image processing to find out the object's face in the image before detecting facial expressions. Adaptive Threshold - Integral Image is a pixel-based segmentation technique, which is local thresholding. This study is intended to split the object of a human face and its background. Face images that will be used later in indoor and outdoor with an image resolution of 300x400 pixels. This study also searched for the best S (kernel) and T (threshold) parameter values by performing 16 experiments. And the best results are obtained, name the image in the room (indoor) the value of S = 1/2 and T = 50, and the image outside the room (outdoor) the value of S = 1/30 and T = 30. Face image segmentation using the Adaptive Threshold - Integral Image robust method of high and low light intensity by setting the S (kernel) and T (Threshold) parameter values, this method is able to split the face object and its background. From the results of the threshold trial using the Adaptive Threshold - Integral Image method for indoor and outdoor images produces a good thresholding by considering the values of the S (kernel) and T (threshold) parameters to give results with a high degree of accuracy, that is indoor images of 96.72%, and outdoor images of 93.59%.<strong></strong></em></p><p><em><strong><br /></strong></em></p>

A Machine Learning-Based System for Detecting Leishmaniasis in Microscopic Images

Background: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, Leishmaniasis is known to be the deadliest parasitic disease globally. Currently, direct visual detection of Leishmania parasite through microscopy is the “gold standard” for the diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm and image processing algorithms for the automatic diagnosis of Leishmaniasis.Methods: The Viola-Jones algorithm was used in this study due to its high recognition speed. This algorithm performs in four stages: detection of Haar-like features, integral image creation, Adaboost training, cascade architecture.Results: A 65% recall and 83% precision was concluded in the detection of macrophages infected with the Leishmania parasite. Also, these numbers were 52% and 35%, respectively, related to amastigotes outside of macrophages.Conclusion: The results contain a fairly high sensitivity, with the specificity being less satisfactory. High processing speed, ease of work, and low expenses are advantages of the presented method compared to other procedures. By adding a few adjustments, this method could be considered a viable option.

A Fast Otsu Thresholding Method Based on an Improved 2D Histogram

The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the optimal threshold, thus reducing the amount of computation. The simulation results demonstrate that the proposed algorithm has better performance in image segmentation, with the increased computational speed and improved real-time capability.

Building Face Detection with Face Divine Proportions

<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in an image based on haar-like features to represent the invariant characteristics of a face. The choice of relevant and more representative features is based on the divine proportions of a face. This technique, widely used in the world of beauty, especially in aesthetic medicine, allows the face to be divided into a set of specific regions according to known mathematical measures. Then we used the Adaboost algorithm for the learning phase. All of our work is based on the Viola and Jones algorithm, in particular their innovative technique called Integral Image, which calculates the value of a Haar-Like feature extracted from a face image. In the rest of this article, we will show that our approach is promising and can achieve high detection rates of up to 99%.</span></p>

CENTRAL AND LOGARITHMIC CENTRAL IMAGE CHORD TRANSFORMATIONS FOR INVARIANT OBJECT RECOGNITION

Pattern descriptors invariant to rotation, scaling, and translation represents an important direction in the elaboration of the real time object recognition systems. In this article, the new kinds of object descriptors based on chord transformation are presented. There are described new methods of image presentation - Central and Logarithmic Central Image Chord Transformations (CICT and LCICT). It is shown that the CICToperation makes it possible to achieve invariance to object rotation. In the case of implementation of the LCICT transformation, invariance to changes in the rotation and scale of the object is achieved. The possibilities of implementing the CICTand LCICToperations are discussed. The algorithms of these operations for contour images are presented. The possibilities of integrated implementation of CICT and LCICT operations are considered. A generalized CICT operation for a full (halftone) image is defined. The structures of the coherent optical processors that implement operations of basic and integral image chord transformations are presented.