Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine

Automated detection and accurate classification of breast tumors using magnetic resonance image (MRI) are very important for medical analysis and diagnostic fields. Over the last ten years, numbers of methods have been proposed, but only few methods succeed in this field. This paper presents the design and the implementation of CAD system that has the ability to detect and classify the tumor of the breast in the MR images. To achieve this, k-mean clustering methods and morphological operators are applied to segment the tumor. The gray scale, Texture and symmetrical features as well as discrete wavelet transform (DWT) are used in feature extracted stage to obtain the features from MR images. Kernel principle components analysis (K-PCA) are also applied as a feature reduction technique and support vectors machine (SVM) are used as a classifier. Finally, the experiments results have confirmed the robustness and accuracy of proposed syste

Detection of incorrect and inappropriateImagefrom Tweets in Social Network

Digital imaging has grown to become the prevalent technology for creating, processing, and storing digital memory and proof. Though this technology brings many leverage, it can be used as a ambiguous tool for covering details and evidences. This is because today digital images can be tampered in such supremacy that forgery cannot be find visually. In fact, the immunity concern of digital content has arisen a long time ago and different methods to verify the efficiency of digital images have been developed. Digital images offer many features for forgery detection algorithm to take precedence of specifically the color and brightness of individual pixels as well as an image�s resolution and format. These properties grant for analysis and similarity between the significance of digital forgeries in an attempt to develop an algorithm for detecting image tampering. This paper presents a technique for image copy or move image forgery detection using Radix Sort, FasterK-means clustering algorithm & DCT

Early Stage Brain Tumor Detection And Classification Using KSVM Algorithm In GUI Window

The brain is central control unit of human body. The tumor is not diagnosed in early stage then it affects the brain means it causes the death of the patient. Magnetic Resonance Image (MRI) doesn’t produce any harmful radiation and it is a better method for area calculation as well as classification based on the grade of the tumor. Nowadays there exists no automatic system to detect and identify the grade of the tumor. This paper proposes brain tumor classification which is divided into four phases as pre-processing, segmentation, feature reduction and extraction, classification. Segmentation of brain Tumor is a one of the basic steps in detection and classification of tumor. The noise is eliminated by using Gaussian filter and canny edge detector is used to detect the tumor area and calculation of tumor area. To segment the tumour K means cluster is used. DWT (Discrete wavelet transform) and GLCM (Grey Level co-occurrence matrix) used for transform and spatial feature extraction and PCA (Principal component analysis) reduces the feature vector to maintain the classification accuracy of brain MRI images. For the performance of MRIs classification, the significant features have been submitted to KSVM (kernel support vector machine). The proposed method is validated on BRATS 2015 dataset and Kaggle dataset. The proposed system will reduce processing time and achieved 99% classification accuracy,98% of sensitivity and 100% of specificity

The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files

Antiforensics techniques and particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice, both techniques are widely researched and developed as considered in the heart of the modern digital era but remain double edged swords standing between the privacy conscious and the criminally malicious, dependent on the severity of the methods deployed. This paper advances the automation of hidden evidence extraction in the context of audio files enabling the correlation between unprocessed evidence artefacts and extreme Steganographic and Cryptographic techniques using the Least Significant Bits extraction method (LSB). The research generates an in-depth review of current digital forensic toolkit and systems and formally address their capabilities in handling steganography-related cases, we opted for experimental research methodology in the form of quantitative analysis of the efficiency of detecting and extraction of hidden artefacts in WAV and MP3 audio files by comparing standard industry software. This work establishes an environment for the practical implementation and testing of the proposed approach and the new toolkit for extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations. The proposed multi-approach automation demonstrated a huge positive impact in terms of efficiency and accuracy and notably on large audio files (MP3 and WAV) which the forensics analysis is time-consuming and requires significant computational resources and memory. However, the proposed automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present) but overall achieve a balance between detecting hidden data accurately along with minimising the false alarms.Comment: Wires Forensics Sciences Under Revie

Improving Classification in Single and Multi-View Images

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fβ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results