Doppler and gray-scale sonographic classification of adnexal torsion

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

Download Full-text

Ultrasonographic Classification of Achilles Tendon Ruptures as a Rationale for Individual Treatment Selection

ISRN Orthopedics ◽

10.5402/2011/869703 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 14

Author(s):

Michael H. Amlang ◽

Hans Zwipp ◽

Adina Friedrich ◽

Adam Peaden ◽

Alfred Bunk ◽

...

Keyword(s):

Achilles Tendon ◽

Tendon Rupture ◽

Achilles Tendon Rupture ◽

Individual Treatment ◽

Reliable Instrument ◽

Tendon Ruptures ◽

Selection For ◽

Sonographic Classification

Purpose. This work introduces a distinct sonographic classification of Achilles tendon ruptures which has proven itself to be a reliable instrument for an individualized and differentiated therapy selection for patients who have suffered an Achilles tendon rupture. Materials and Methods. From January 1, 2000 to December 31, 2005, 273 patients who suffered from a complete subcutaneous rupture of the Achilles tendon (ASR) were clinically and sonographically evaluated. The sonographic classification was organized according to the location of the rupture, the contact of the tendon ends, and the structure of the interposition between the tendon ends. Results. In 266 of 273 (97.4%) patients the sonographic classification of the rupture of the Achilles tendon was recorded. Type 1 was detected in 54 patients (19.8%), type 2a in 68 (24.9%), type 2b in 33 (12.1%), type 3a in 20 (7.3%), type 3b in 61 (22.3%), type 4 in 20 (7.3%), and type 5 in 10 (3.7%). Of the patients with type 1 and fresh ASR, 96% () were treated nonoperative-functionally, and 4% () were treated by percutaneous suture with the Dresden instrument (pDI suture). Of the patients classified as type 2a with fresh ASR, 31 patients (48%) were treated nonoperatively-functionally and 33 patients (52%) with percutaneous suture with the Dresden instrument (pDI suture). Of the patients with type 3b and fresh ASR, 94% () were treated by pDI suture and 6% () by open suture according to Kirchmayr and Kessler. Conclusion. Unlike the clinical classification of the Achilles tendon rupture, the sonographic classification is a guide for deriving a graded and differentiated therapy from a broad spectrum of treatments.

Download Full-text

LAND-USE CLASSIFICATION OF GRAY-SCALE AERIAL IMAGES USING PROBABILISTIC NEURAL NETWORKS

Transactions of the ASAE ◽

10.13031/2013.17598 ◽

2004 ◽

Vol 47 (5) ◽

pp. 1813-1819 ◽

Cited By ~ 8

Author(s):

D. Ashish ◽

G. Hoogenboom ◽

R. W. McClendon

Keyword(s):

Neural Networks ◽

Land Use ◽

Aerial Images ◽

Land Use Classification ◽

Probabilistic Neural Networks ◽

Gray Scale

Download Full-text

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Journal of Healthcare Engineering ◽

10.1155/2018/2849567 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Alejandra Cruz-Bernal ◽

Martha M. Flores-Barranco ◽

Dora L. Almanza-Ojeda ◽

Sergio Ledesma ◽

Mario A. Ibarra-Manzano

Keyword(s):

Digital Image ◽

Visual Inspection ◽

Experimental Results ◽

Gray Scale ◽

High Expectation ◽

Weak Classifier ◽

White Region ◽

Gray Scale Image ◽

Global Representation

In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

Download Full-text