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.
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being ...with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation‐based selection method and as the output, the best features are selected. These selected features are validated through feed‐forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.
3D convolutional neural network (CNN) architecture is proposed for tumor extraction. The pretrained VGG19 CNN model is utilized for feature extraction. Correlation‐based along FNN‐based best features are selected. Results are validated for segmentation and classification steps.
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.
Acute Leukemia is a life‐threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly ...and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer‐aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub‐types that will definitely assist pathologists.
This research proposed a method for the classification of Acute Lymphoblastic Leukemia (ALL) into its subtypes and reactive bone marrow (Normal) in stained bone marrow images using deep learning techniques with convolutional neural networks.
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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•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.
Alzheimer’s disease (AD) is an incurable neurodegenerative disorder accounting for 70%–80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity ...associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.
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