Prostate cancer is the commonest male cancer and the second leading cause of cancer-related death in men. Over many decades, prostate cancer detection represented a continuous challenge to urologists. Although all urologists and pathologists agree that tissue diagnosis is essential especially before commencing active surgical or radiation treatment, the best way to obtain the biopsy was always the big hurdle. The heterogenicity of the tumor pathology is very well seen in its radiological appearance. Ultrasound has been proven to be of limited sensitivity and specificity in detecting prostate cancer. However, it was the only available targeting technique for years and was used to guide biopsy needle passed transrectally or transperineally. Magnetic Resonance Imaging (MRI) has revolutionized the process with the advent of its multiparametric imaging (mp MRI) where the prostate is evaluated by different MRI techniques and the likelihood of the detected lesion is scored using the new prostate imaging-reporting and data system (PIRADS) scoring. Despite the improved detection of clinically significant prostate cancer by mpMRI, the ideal way to target the area of suspicion detected by mpMRI is the next level of challenge. In this review article, we will discuss the recent methods of targeting and focus on the different platforms used to integrate the mpMRI static images with the real-time US scanning in what is called (US-MRI fusion techniques).
Examining of 80 feces samples showed that 31 samples of the house and stray cats harbored either single or mixed infection with eight species of parasites and protozoa with a total infection rate 38.75 %.The results on parasite classes are: Toxocara cati (5%), Ancylostoma tubeforme (3.75%), Capillaria felis(3.75%), Isospora sp.(10%), Cryptosporidium parvum(3.75%), Cryptosporidium muris (6.25%), Toxoplasma gondi (3.75%), Giardia sp.(2.5%) infection from feces of cats that showed single, double and triple infections. Our findings revealed the risk for public health, thus preventive measures should be implemented.
Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
... Show MoreFor several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.
The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.
Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.
The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple
... Show MoreGlaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to other eye illness such as myopia and cataracts. The impact of glaucoma can’t be cured; The Disc Damage Likelihood Scale (DDLS) can be used to assess the Glaucoma. The proposed methodology suggested simple method to extract Neuroretinal rim (NRM) region then dividing the region into four sectors after that calculate the width for each sector and select the minimum value to use it in DDLS factor. The feature was fed to the SVM classification algorithm, the DDLS successfully classified Glaucoma d
HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
The adsorption process of 5-Fluorouracil (5FU) drugs on Aluminum nitride nanotubes surface (AlNNTs) have been evaluated through density functional theory (DFT). The DFT results show that the interaction of AlNNTs with the F atoms of 5FU drugs is strong due to the fact that the amount of adsorption energy was about − 29.65 kcal.mol−1. Conversely, the interaction of the 5FU through O atoms with the AlNNTs was weaker due to the lower value of adsorption energy. Also, based on the values of Gibbs free energy, the 5FU adsorption on the surfaces of AlNNTs was spontaneous. In addition, based on natural bond orbital (NBO) analysis, the direction of charge transfer was from fluorine’s σ orbitals of the drug to nitrogen’s and aluminum’s n*
... Show More