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BACKGROUND: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is of utmost importance to avoid untoward incidents that may even lead to death. Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. METHODS: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on the CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method. Secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored, and finally, a feature selection method, Principal Component Analysis (PCA), has been introduced in the AlexNet-SVM classifier model, and its efficacy is also explored. These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. RESULTS: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86% and sensitivity and specificity of 0.9986 for the detection of ICH in the brain CT images. CONCLUSION: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. The proposed simplified deep learning framework also manifests its ability as a screening tool to assist the radiological trainees in the accurate detection of ICH.

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Objectives This work describes the design and development of a four-channel near-infrared spectroscopy system to detect the oxygenated and deoxygenated hemoglobin concentration changes in the brain during various motor tasks. Methods The system uses light-emitting diodes corresponding to two wavelengths of 760 nm and 850 nm sensitive to deoxygenated and oxygenated hemoglobin concentration changes, respectively. The response is detected using a photodetector with an integrated transimpedance amplifier. The system is designed with four channels for functional near-infrared spectroscopy (fNIRS) signals acquisition. Two experiments were conducted to demonstrate the ability of the system to detect the changes in hemodynamic responses of different tasks. In the first experiment, the hemodynamic changes during motor execution and imagery of right- and left-fist clenching tasks were acquired by the developed system and validated against a standard multichannel NIRS system. In another experiment, the fNIRS signals during rest and motor execution of right-fist clenching task were acquired using the system and classified. Results The results demonstrate the ability of the designed system to detect the brain hemodynamic changes during various tasks. Also, the activation patterns obtained by the developed system with a minimum number of channels are on par with those obtained by the commercial system. Conclusions The developed four-channel NIRS system is user-friendly and has been designed with inexpensive components, unlike the commercially available NIRS instruments that are cumbersome and expensive.

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INTRODUCTION: Suturing is an integral part of all surgeries. In minimal access surgery, the force exerted is based only on visual perception (tautness of the thread and degree of tissue deformation). An unbalanced suture force can cause tissue rupture or cut-through resulting in avoidable morbidity and mortality. There is a need to find ways of improving surgical dexterity and finesse without adversely affecting patient outcomes. AIM: We aimed to calculate the knot-tying force in minimal access pancreatic surgery (MAPS) performed by experienced surgeons (ES) and use this information to develop a surgical suturing model to train the surgical trainees. We have developed a firmware for force sensor calibration and post-data analysis, using which we aimed to compare the differences in forces applied by a trainee as compared to ES. RESULTS: Our technology showed that, as compared to the ES, the trainee's (TS) knot was unbalanced with significant differences in force applied per knot for each of the knots (P < 0.01). The shape of the Force curve for each suture was also different for the TS as compared to the ES. After using the training tool, the forces applied by the TS and the Force curve for the whole suture were similar to those of the ES. CONCLUSION: Our firmware promises to be an excellent training tool for organ anastomosis. Considering the complexity and likely complications of MAPS, it is a sine qua non that the surgeon be highly experienced and skilled. Surgical simulation is attractive because it avoids the use of patients for skills practice and provides relevant technical training for trainees before they can safely operate on humans.

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Cancer therapy is one of the several new applications which use nanosecond and subnanosecond high voltage pulses. New treatment based on electromagnetic (EM) fields have been developed as non-surgical and minimally invasive treatments of tumors. In particular, subnanosecond pulses can introduce important non-thermal changes in cell biology, especially the permeabilization of the cell membrane. The motivation behind this work is to launch intense subnanosecond pulses to the target (tumors) non-invasively. This works focuses on the design of a compact intense pulsed EM radiating antenna. In tense EM waves radiated at the first focal point of the Prolate Spheroidal Reflector (PSR) are focused at the second focal point where the target (tumor) is present. Two antennas with PSR but fed with different compact wave radiator are designed to focus pulsed field at the second focal point. The PSR with modified bicone antenna feed and PSR with elliptically tapered horn antenna feed are designed. The design parameters and radiation performance are discussed.

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