Cancer Cell Detection in Bone Using ANN

A vigorous disease is bone cancer results in deaths of many people. The identification and classification system must be done at its early stage to diagnose. The early detection plays an important role to safe guard the patient from death. And also cancer categorization is one of the toughest tasks in clinical analysis. This paper deals with MR images of various patients used to identify the tumor and classify cancer using Artificial Neural Network algorithm. The proposed methodology uses filtering as preprocessing techniques followed by gray conversion and other image processing methods like edge detection, morphological operation, segmentation, feature extraction and classification are prepared for the identification of bone cancer. By this method time required is reduced for identification and classification of bone cancer.

Detection of Lung Cancer Cells in Solutions Using a Terahertz Chemical Microscope

Cancer genome analysis has recently attracted attention for personalized cancer treatment. In this treatment, evaluation of the ratio of cancer cells in a specimen tissue is essential for the precise analysis of the genome. Conventionally, the evaluation takes at least two days and depends on the skill of the pathologist. In our group, a terahertz chemical microscope (TCM) was developed to easily and quickly measure the number of cancer cells in a solution. In this study, an antibody was immobilized on a sensing plate using an avidin-biotin reaction to immobilize it for high density and to improve antibody alignment. In addition, as the detected terahertz signals vary depending on the sensitivity of the sensing plate, the sensitivity was evaluated using pH measurement. The result of the cancer cell detection was corrected using the result of pH measurement. These results indicate that a TCM is expected to be an excellent candidate for liquid biopsies in cancer diagnosis.

High-speed single perceptron for optical neural networks based on microcombs

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1, 2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.