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Go to Editorial ManagerOne of the most common causes of mortality worldwide is Lung cancer, an early diagnosis crucial for a patient’s survival and recovery. Automated segmentation of lung lesions in chest CT has become a pre-eminent focal point for research, particularly with the development of hybrid methods combining traditional image processing with advanced deep learning methods such as CNN. These hybrid approaches aim to minimize individual methods limitations by controlling their merge strengths to enhance segmentation efficiency, precision, and clinical utility. This review comprehensively analyzes different hybrid techniques, such as deep learning improved by rule-based systems, multi-scale feature extraction, and ensemble learning. As well as inspect their clinical effect, particularly in improving diagnostic accuracy and optimizing treatment procedures. Despite their possibility, these approaches still face significant challenges, such as computational complexity, data requirements, and the requirement for explainable AI (XAI). Upcoming advancements in lung lesion segmentation will focus on refining these models to achieve faster processing, improved accuracy, and integration with diagnostic tools to protect transparency and ethical considerations.
This paper presents a dual wide-band band pass filter (DWB-BPF) by using two parallel, symmetrical micro-strip lines loaded by a centered resonator, consisting of a T- and a triangle-shaped geometry, attached at the lower and upper ends, respectively. The filter reveals good performance and both the passbands can be independently controlled by adjusting specific parts of the filter. The proposed BPF is simulated by using CST microwave studio package and the simulated result is verified experimentally with good agreement between the two results. The fabricated prototype BPF demonstrates two passbands located at 2.3 GHz and 6.35 GHz center frequencies with 39% and 23.6% of 3-dB fractional bandwidth (FBW), respectively and a good insertion and return losses. The designed BPF can be targeted for wireless local area network (WLAN), WIFI and satellite communication systems.
Diabetes is a long-term medical condition that impacts the way your body converts food into energy, it has the potential to lead to several severe health complications, such as heart disease, stroke, vision impairment, kidney issues, and nerve damage. Nevertheless, individuals with diabetes can lead extended and healthy lives with effective management. The goal of diabetes treatment is to keep your blood sugar levels within a healthy range. So Glucose measurement is an important part of diabetes management. It allows people with diabetes to track their blood sugar levels and make adjustments to their diet and medication as needed. Morning fasting blood glucose typically falls within the range of (70 mg/dL) to (110 mg/dL), while after a meal, blood glucose levels should ideally be below (140 mg/dL). In this proposed work an Arduino-based noninvasive glucose measurement device is proposed. Non-invasive glucose measurement devices do not require the user to prick their finger to draw blood. A Red Laser (RL) technique, is employed, this method surpasses the other invasive approach and non-invasive methods in terms of superiority. Since invasive techniques can be painful and expensive. This paper describes a new way to measure blood sugar levels without having to prick your finger. The method uses a red laser to shine light through the skin and measure how much the light is bent. The amount of bending tells the device how much sugar is in the blood. Numerous tests and experimental outcomes have been produced to demonstrate the exceptional accuracy of the proposed method.