Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous cells are abnormal areas often growing in any part of human body that are ...life-threatening. Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure. Even though modality has different considerations, such as complicated history, improper diagnostics and treatement that are main causes of deaths. The aim of the research is to analyze, review, categorize and address the current developments of human body cancer detection using machine learning techniques for breast, brain, lung, liver, skin cancer leukemia. The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques. Several state of art techniques are categorized under the same cluster and results are compared on benchmark datasets from accuracy, sensitivity, specificity, false-positive metrics. Finally, challenges are also highlighted for possible future work.
Violence is a critical social problem and demands to evaluate through computer vision approaches. At present, the incidences of violent actions get grown in the community, particularly in public ...places due to several economic and social causes. Moreover, our society’s populations are increasing day by day and it is challenging to keep citizens within limits as well as monitoring human activities in crowd is too hard. Thus, government organizations including local bodies, require examining such occurrences through smart surveillance. In this research, a lightweight computational architecture has been presented to classify non-violent and violent activities. A model has been proposed to extract time-based features using smart devices, high-speed wireless networks and cloud servers to classify real-time human activities. For this purpose, a deep learning-based model is employed to detect violent activities and assist the stakeholders in exposing such activities in real-time. Convolutional long short-term memory (Conv-LSTM) is employed to extend fully connected LSTM (FC-LSTM) to capture the frame and detect violent actions. The proposed model accomplished 95.16% validation accuracy using a standard crowd anomaly dataset.
In various fields, the internet of things (IoT) gains a lot of popularity due to its autonomous sensors operations with the least cost. In medical and healthcare applications, the IoT devices develop ...an ecosystem to sense the medical conditions of the patients' such as blood pressure, oxygen level, heartbeat, temperature, etc. and take appropriate actions on an emergency basis. Using it, the healthcare-related data of patients is transmitted towards the remote users and medical centers for post-analysis. Different solutions have been proposed using Wireless Body Area Network (WBAN) to monitor the medical status of the patients based on low powered biosensor nodes, however, preventing increased energy consumption and communication costs are demanding and interesting problems. The issue of unbalanced energy consumption between biosensor nodes degrades the timely delivery of the patient's information to remote centers and gives a negative impact on the medical system. Moreover, the sensitive data of the patient is transmitting over the insecure Internet and prone to vulnerable security threats. Therefore, data privacy and integrity from malicious traffic are another challenging research issue for medical applications. This research article aims to a proposed secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT), which primary objective is to decrease the communication overhead and energy consumption between biosensors while transmitting the healthcare data on a convenient manner, and the other hand, it also secures the medical data of the patients against unauthentic and malicious nodes to improve the network privacy and integrity. The simulated results exhibit that the proposed framework improves the performance of medical systems for network throughput by 18%, packets loss rate by 44%, end-to-end delay by 26%, energy consumption by 29%, and link breaches by 48% than other states of the art solutions.
An entity's existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be ...state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models-Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)-detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.
In agriculture farming business, plant diseases are one of the reasons for the financial deficits around the globe. It is the fundamental factor, as it causes significant abatement in both capacity ...and quality of the growing crops. In plants, fruits are amongst the major sources of nutrients, however, there exists a wide range of diseases which adversely affect both quality and production of the fruits. To overcome such predicament, computer vision (CV) based methods are introduced. These methods are quite effective, which not only detect the diseases/infections at the early stages but also assign them a label. In this article, we propose a deep convolutional neural network-based method for the diseases classification of different fruits’ leaves. Initially, the deep features are extracted by utilizing pre-trained deep models including VGG-s and AlexNet, which are later fine-tuned by employing a concept of transfer learning. A multi-level fusion methodology is also proposed, prior to the selection step, based on an entropy-controlled threshold value - calculated by averaging the selected features. The resultant final feature vector is later fed into a host classifier, multi-SVM. Five different diseases are considered for experiments including apple black rot, apple scab, apple rust, cherry powdery mildew, and peach bacterial spots, which are collected from a plant village dataset. Classification results clearly reveal the improved performance of proposed method in terms of sensitivity (97.6%), accuracy (97.8%), precision (97.6%), and G-measure (97.6%).
Human action recognition (HAR) has gained much attention in the last few years due to its enormous applications including human activity monitoring, robotics, visual surveillance, to name but a few. ...Most of the previously proposed HAR systems have focused on using hand-crafted images features. However, these features cover limited aspects of the problem and show performance degradation on a large and complex datasets. Therefore, in this work, we propose a novel HAR system which is based on the fusion of conventional hand-crafted features using histogram of oriented gradients (HoG) and deep features. Initially, human silhouette is extracted with the help of saliency-based method - implemented in two phases. In the first phase, motion and geometric features are extracted from the selected channel, whilst, second phase calculates the Chi-square distance between the extracted and threshold-based minimum distance features. Afterwards, extracted deep CNN and hand-crafted features are fused to generate a resultant vector. Moreover, to cope with the curse of dimensionality, an entropy-based feature selection technique is also proposed to identify the most discriminant features for classification using multi-class support vector machine (M-SVM). All the simulations are performed on five publicly available benchmark datasets including Weizmann, UCF11 (YouTube), UCF Sports, IXMAS, and UT-Interaction. A comparative evaluation is also presented to show that our proposed model achieves superior performances in comparison to a few exiting methods.
•Motion and Geometric features are extracted for human flow estimation and silhouette extraction.•Deep CNN and hand crafted features are fused through parallel approach.•Entropy-controlled Chi-square approach is proposed for best features selection.•Experiments are performed on several well-known datasets.
In digital mammography, finding accurate breast profile segmentation of women's mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer ...due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing.
We propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images.
To assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth.
Our approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.
•A technique for copy-move forgery detection in images is proposed via SWT and DCT.•The technique utilizes SWT based features for exposing forgeries in digital images.•The dimension of feature ...vectors is reduced by applying block DCT to each block.•The results show that the technique outperforms in terms of TDR and FDR.
In this era, due to the widespread availability of digital devices, various open source and commercially available image editing tools have made authenticity of image contents questionable. Copy-move forgery (CMF) is a common technique to produce tampered images by concealing undesirable objects or replicating desirable objects in the same image. Therefore, means are required to authenticate image contents and identify the tampered areas. In this paper, a robust technique for CMF detection and localization in digital images is proposed. The technique extracts stationary wavelet transform (SWT) based features for exposing the forgeries in digital images. SWT is adopted because of its impressive localization properties, in both spectral and spatial domains. More specifically approximation subband of the stationary wavelet transform is utilized as this subband holds most of the information that is best suited for forgery detection. The dimension of the feature vectors is reduced by applying discrete cosine transform (DCT). To evaluate the proposed technique, we use two standard datasets namely, the CoMoFoD and the UCID for experimentations. The experimental results reveal that the proposed technique outperforms the existing techniques in terms of true and false detection rate. Consequently, the proposed forgery detection technique can be applied to detect the tampered areas and the benefits can be obtained in image forensic applications.
In this article, we implement an action recognition technique based on features fusion and best feature selection. In the proposed method, HSI color transformation is performed in the first step to ...improve the contrast of video frames and then extract their motion features by optical flow algorithm. The frames fusion approach extracts the moving regions that find out by optical flow. After that, extract shape and texture features fused by a new parallel approach name length control features. A new Weighted Entropy-Variances approach is applied to a combined vector and selects the best of them for classification. Finally, features are passed in M-SVM for final features classification into relevant human actions. The experimental process is conducted in four famous action datasets- Weizmann, KTH, UCF Sports, and UCF YouTube, with recognition rate 97.9%, 100%, 99.3%, and 94.5%, respectively. Experimental results show that the proposed scheme performed significantly sound output concerning listed methods.
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•A sparse activation function is applied to find out the locations of active regions.•Fused two segmented frames using multiplication law of probability.•Features are fused using a parallel approach name length control features (LCF).•Weighted Entropy-Variance controlled approach is proposed for features selection.
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