Smartphone Based Image Color Correction for Color Blindness

Color blind is a type of Color Vision Deficiency, which is the inability that a person could not realize the differences between some colors. There are three types of color blindness: Monochromacy, Dichromacy, and Anomalous Trichromacy. Color blind cannot be cured. Today, technology gets up with solutions to help people with color blindness to see the image and distinguish between the different colors using some algorithms. This paper presents a smartphone based experimental comparison of color correction algorithms for Dichromacy color-blind viewers. This comparison includes LMS Daltonization algorithm, Color-blind Filter Service (CBFS) algorithm, LAB color corrector algorithm, and the shifting color algorithm. The description of the smartphone based implementation details and parameters settings of these algorithms is presented. An application interface is implemented to enable the user to choose the algorithm that gives the most appropriate results. The results of these algorithms are compared to see their strength and weakness.</p

Improved capacity Arabic text watermarking methods based on open word space

Digital watermarking is used to protect text copyright and to detect unauthorized use. In this paper, two invisible blind watermarking methods for Arabic text are proposed. Since the pseudo-space is very small space used to force the connected characters to be isolated, it is added to the word space to hide binary bit “0” or “1”. In the first proposed method, the pseudo-space is inserted before and after normal word space based on dotting feature in Arabic text. The second proposed method inserts the pseudo-space and other three small or zero width spaces to increase the capacity, where the presence of them indicates bit “1” and the absence indicates bit “0”. The comparative results obtained by testing the proposed methods with some of existing watermarking methods using variable size text samples with different watermark lengths. The experiments show that the proposed methods have the highest capacity and higher imperceptibility than other watermarking techniques from the literature. The robustness of the proposed methods is tested under most of possible text attacks. They are robust against electronic text attacks such as: copying and pasting, text formatting and text tampering for tampering ratio up to 84%. Keywords: Arabic text watermarking, Capacity, Robustness, Imperceptibilit

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets