Novel Low-Noise CMOS Bioamplifier for the Characterization of Neurodegenerative Diseases.

Conducting cells of the heart and nerve cells of the brain are having the ability to generate and transmit electrical signals. Recording of neural signals became an important research issue for better analysis and better control of neurological functions by using implantable devices. In neural recording systems, the most critical part is the power constraint neural amplifier. The major challenges of neural front ends are low power dissipation and low input-referred noise. This work describes a low-noise amplifier that uses Metal Oxide Semiconductor bipolar pseudo-resistor elements to amplify signals from 0.03 millihertz to 8.4 kilohertz. This design is suitable for neurodegenerative disorders like Parkinson's disease and Alzheimer's. This topology reduces major noise in low-frequency circuits. By choosing input devices as PMOS transistors and also by properly sizing the devices, flicker noise is reduced. Noise and power trade-off is quantified by calculating noise efficiency factor (NEF) which is improved by using the proposed design. The circuit is implemented in 180 nm technology and is operated with a dual power supply range of ±2.5 V.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.