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Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet’s advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.

This proposed work is an attempt to design an advance vehicle security system that uses GPS and GSM system to prevent theft and to determine the exact location of vehicle. Today theft is happening on the parking or in some insecure place. The safety of the vehicle is exceptionally essential. The advance vehicle security system is designed using GPS and GSM technology. The system contains GPS module, GSM modem, Infrared sensors, DTMF tone decoder, 8051 microcontroller, relay switch, paint spray and high voltage mesh. GPS system track the current location of vehicle, there are two types of tracking used one is online tracking and other is offline tracking. GSM system is also installed in the vehicle for sending the information to the user because GPS system can only receive the vehicle location information from satellites. In case of accident this system automatically sends the message for help to ones relatives. The preventive measures like engine ignition cutoff, Fuel supply cutoff, electric shock system (installed on steering wheel) and paint spray system are installed in the vehicle which is controlled using user GSM Mobile. This complete system is designed taking in consideration the low range vehicles to provide them extreme security.

In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide cancer statistics, colorectal cancer is one of the most common cancers. The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date. The traditional manual process is dependent on the expertise of the medical practitioner. In this paper, a two-stage classification is proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video are extracted and are rated as significant if it contains a polyp, and these results are then aggregated in a second stage to come to an overall decision concerning the final classification of that frame to be neoplastic and non-neoplastic. In doing so, a comparative study is being made by considering the applicability of deep learning to perform this two-stage classification. The CNN models namely VGG16, VGG19, Inception V3, Xception, GoogLeNet, ResNet50, ResNet100, DenseNet, NASNetMobile, MobilenetV2, InceptionResNetV2 and fine-tuned version of each model is evaluated. It is observed that the VGG19 model is the best deep learning method for colonoscopy image diagnosis.

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