LUNG CANCER DETECTION TECHNIQUE BASED ON SURF DESCRIPTOR AND KNN ALGORITHMS

In this century, lung cancer is undoubtedly one of the major serious health problems, and one of the leading causes of death for women and men worldwide. Despite advances in treating lung cancer with unprecedented products of pharmaceutical and technological advances, mortality and morbidity rates remain a major challenge for oncologists and cancer biologists. Thus, there is an urgent need to provide early, accurate, and effective diagnostic techniques to improve the survival rate and reduce morbidity and mortality related to lung cancer patients. Therefore, in this paper, an effective lung cancer screening technique is proposed for the early detection of risk factors for lung cancer. In this proposed technique, the powerful acceleration feature Speeded up robust feature (SURF) was used to extract the features. One of the machine learning methods was used to detect cancer by relying on the k nearest neighbor (KNN ) method, where the experimental results show an effective way to discover SURF features and tumor detection by relying on neighborhoods and calculating the distance using KNN. As a result, a high system sensitivity performance success rate of 96% and a system accuracy of 99% has been achieved.

Single and multiple regions duplication detections in digital images with applications in image forensic

‘With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.

Openfinger: Towards a Combination of Discriminative Power of Fingerprints and Finger Vein Patterns in Multimodal Biometric System

Multimodal biometric systems are nowadays considered as state of the art subject. Since identity establishment in everyday situations has become very significant and rather difficult, there is a need for reliable means of identification. Multimodal systems establish identity based on more than one biometric trait. Hence one of their most significant advantages is the ability to provide greater recognition accuracy and resistance against the forgery. Many papers have proposed various combinations of biometric traits. However, there is an inferior number of solutions demonstrating the use of fingerprint and finger vein patterns. Our main goal was to contribute to this particular field of biometrics.In this paper, we propose OpenFinger, an automated solution for identity recognition utilizing fingerprint and finger vein pattern which is robust to finger displacement as well as rotation. Evaluation and experiments were conducted using SDUMLA-HMT multimodal database. Our solution has been implemented using C++ language and is distributed as a collection of Linux shared libraries.First, fingerprint images are enhanced by means of adaptive filtering where Gabor filter plays the most significant role. On the other hand, finger vein images require the bounding rectangle to be accurately detected in order to focus just on useful biometric pattern. At the extraction stage, Level-2 features are extracted from fingerprints using deep convolutional network using a popular Caffe framework. We employ SIFT and SURF features in case of finger vein patterns. Fingerprint features are matched using closed commercial algorithm developed by Suprema, whereas finger vein features are matched using OpenCV library built-in functions, namely the brute force matcher and the FLANN-based matcher. In case of SIFT features score normalization is conducted by means of double sigmoid, hyperbolic tangens, Z-score and Min-Max functions.On the side of finger veins, the best result was obtained by a combination of SIFT features, brute force matcher with scores normalized by hyperbolic tangens method. In the end, fusion of both biometric traits is done on a score level basis. Fusion was done by means of sum and mean methods achieving 2.12% EER. Complete evaluation is presented in terms of general indicators such as FAR/FRR and ROC.

A Vision based Indian Traffic Sign Classification

In this paper, an algorithm is proposed to classify the Indian traffic sign as mandatory cautionary and informatory class. In order to complete the task, system extracted the speed up robust features (SURF) from the Indian traffic sign data, and exploited these features to train support vector machine (SVM) algorithm. Combination of SURF features and SVM classifier makes system robust for scale variation, rotation, translation and illumination variation as well as generalization is achieved. Dimension of features have been reduced by choosing a sub set of features. Whisker and box plot visualization utilized to understand the features data. Whisker plot visualization concluded about the range, skewness, median and outliers of feature data therefore, it makes the system capable to keep good features and back out from irrelevant features. Feature refinement reduces the computational complexity. The results evaluated narrate that the overall performance of proposed algorithm is efficient.