كلية هندسة النفط والغاز — جامعة البصرة للنفط والغاز | البوابة الأكاديمية والتعليمية الرسمية لقطاع الطاقة والنفط والغاز

Detection and correction the cyber-attack of electroencephalogram signals using deep learning model

📖 Neural Computing and Applications

HEPATITIS C VIRUS STAGES CLASSIFICATION USING TRANSIT SEARCH ALGORITHM AND LONG SHORT TERM MEMORY

📖 Kufa Journal of Engineering

ENHANCED BREAST TOMOSYNTHESIS RECONSTRUCTION USING DISTANCE-DRIVEN MAXIMUM LIKELIHOOD EXPECTATION MAXIMIZATION TECHNIQUE

📖 Kufa Journal of Engineering

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts.