Breast cancer is detected by identifying malignancy on breast tissue. Emerging technologies in medical image processing are used to interpret histopathology images. For analyzing medical imaging and pathological data, modified deep neural networks are being used. Automatic detection of malignancy is usually achieved in deep learning by capturing features from a convolutional neural network (CNN) and

Recent advancements in deep learning have achieved great success in building a reliable computer-aided diagnosis (CAD) system. In this work, a novel deep-learning architecture, named receptive field regularized V-net (RFR V-Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution

Effective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate

Alzheimer's disease is a progressive neurodegenerative fatal disease characterized by a decrease in mental functions. Although there is no definitive treatment for the disease, there are some treatment methods that delay the course of the disease in case of early diagnosis. Therefore, early diagnosis and classification of the disease are important to determine the most appropriate treatment. The most

With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19

This article introduces two new designs of transmit/receive switches for 7-Tesla magnetic resonance imaging. Both designs based on a microstripline coupler technology. In the first design, a different single frequency signals can be handled to/from a radio frequency coil using smart tuning network. In the second design, a dual-tuned 1H/23Na transmit/receive switch is designed to handle a dual resonant

The goal of the article shows a new technique to provide three-dimensional (3D) atlas to be used for a preoperative planning for safe resection of jugular-foramen tumor. A surgical-planning drawing was created based on the test results to provide precise guidance to neurosurgeons. Using magnetic resonance imaging and computed tomography scans of the head as guide images, we generated a 3D surgical

We propose optimisation based algorithm using wavelet decomposition for segmentation of echocardiography images. The objective of the proposed method is to find an optimal threshold value for segmentation. This threshold value is used for separating the left ventricle by segmenting wavelet decomposed coefficients. The proposed method evaluates the optimal value of threshold using a nonlinear derivative

Classification of oral cysts is a crucial task as the similarity between cysts exists which requires a computer-aided diagnosis system. Panoramic dental image is one of the widely used images to identify dental cyst, periodontal bone defects, periapical lesions, and pathological jaw lesions. This article proposes a modified LeNet architecture in a convolutional neural network for classifying the oral

An individualized model that predicts trial-by-trial working memory performance is instrumental for personalized interventions. Here, we propose a single-trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non-correct during a working memory task, N-back. We used event-related (de-)synchronization (ERD and ERS) prior to

As a potential imaging technique, electrical impedance tomography (EIT) is advantageous for reconstructing conductivity distribution. However, due to insufficient measurement, visualization is inevitably an ill-posed inverse problem. Moreover, reconstruction quality is affected by noise. To address these challenges, a novel variational model with an Lp-norm as fidelity and a hybrid total variation

Computer-aided classification of pathological images is of the great significance for breast cancer diagnosis. In recent years, deep learning methods for breast cancer pathological image classification have made breakthrough progress, becoming the mainstream in this field. To capture more discriminant deep features for breast cancer pathological images, this work introduces a novel attention high-order

This study was conducted to comparatively analyze the performance of generalized autocalibrating partially parallel acquisition (GRAPPA) algorithms according to the kernel types, which has not yet been reported. A GRAPPA algorithm using one 3D kernel or other kernels was used to reconstruct the undersampled 3D MRSI data along three or two PE directions. To quantitatively assess the quality of the reconstruction

Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer-aided pathology research. In this article, Mask RCNN is presented for the automatic detection of nuclei on high-resolution histopathological images

Colon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used

The automatic detection and location of the tumor regions in lung images is more important to provide timely medical treatments to patients in order to save their lives. In this article, machine learning-based lung tumor detection, classification and segmentation algorithm is proposed. The tumor classification phase first smooth the source lung computed tomography image using adaptive median filter

A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to

Alzheimer's disease is a subject of substantial concern with ample scope for novel discoveries in the field of modern computational medical study for its reputation of being one of the most exorbitant and life-threatening neurodegenerative diseases of the current age. The prime objective of this research is to develop a system that can automatically detect three stages of Alzheimer's disease—Alzheimer's

The aim of this study was to evaluate the reproducibility of voxel-wise temporal signal-to-noise ratio (tSNR) on repeated scans across runs, sessions, and days. A group of 21 participants was scanned 16 times (4 runs per session, 2 sessions per day, 2 separate days) in a functional magnetic resonance imaging (fMRI) study on a 3T Philips Achieva scanner. For each run, we calculated t-value and tSNR

A brain tumor is considered one of the deadliest forms among all types of cancer due to its aggressive nature leading to patients’ low survival rate. Detection and classification of brain tumors have a significant impact on treatment planning and patient survival. The significance and importance of this work lie in the formulation of several probabilistic features that represent higher-level probabilistic

In the last decade, the public health problem is the primary consciousness worldwide, and cancer is especially the central issue. Further, skin cancer comes in the top-3 of the world's most common cancer. We have proposed an efficient convolutional neural network (CNN) model that identifies skin cancer problems accurately. Although dataset HAM10K is used for the classification problem, its samples

Considering the prevailing scenario of COVID-19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare

Gliomas are one of the most dangerous brain tumours, which are life-threatening in their highest grade. Manual diagnosis and detection of brain tumours are time-consuming. Thus, early and accurate detection of such tumours becomes essential for its prognosis. In this work, we aim to develop a fully automated glioma grade classification framework. The proposed method aims to learn relevant and non-redundant

This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well-organized classifiers, the convolutional neural network (CNN) and feature-based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET

To improve the diagnosis efficiency of diseases related to retinal blood vessels, this article proposes a novel retinal blood vessels segmentation algorithm by combining the advantages of convolutional neural network (CNN) and cascade forest (CF). Firstly, we use the contrast limited adaptive histogram equalization (CLAHE) algorithm to enhance the color fundus retinal image. Secondly, we randomly select

Glaucoma is a common ocular disorder, inflicting blindness in millions of people, early detection of which can reduce adverse outcomes. This paper presents a two-stage deep learning framework UNet-SNet, for glaucoma detection. Initially, each fundus image is segmented into GMM super pixels and the Region of Interest (RoI) is separated by Cuckoo Search Optimization (CSO). In the first stage, a regularized

Patellar dislocation is a disorder in the human knee where patella slips out of its expected position. The orthopedic experts need to manually measure the parameters indicating patellar instability from knee scans, which is a laborious and time-consuming task. An automated method for measuring these parameters can resolve the issue of inter-observer and intra-observer variations and significantly lessen

The symptoms of lung cancer mainly manifest as lung nodules. The recognition and diagnosis of a ground glass nodule (GGN) are relatively difficult, and its image features are not easy to extract. To improve the accuracy of segmentation and invasive classification of GGN, a region adaptive Markov random field (MRF) model and a two-channel integrated network based on densely connected convolutional neural

Image enhancement has been paid great attention in several areas, and various image enhancement approaches are introduced by the researchers to enhance the images clearer from the degraded images. Accordingly, this paper presents the proposed ACSO-based IE-CGAN for image enhancement. Initially, an effective noisy pixel identification based on image enhancement is developed, which employs circular-based

In the treatment of lung cancer, bedside monitoring plays an important role. X-ray computed tomography, as a mostly used technique, cannot provide medical surveillance for lung cancer patients in real-time. In comparison, electrical impedance tomography (EIT) has the potential to solve the issue by visualizing human respiratory system; however, its spatial resolution is too low to locate the real pulmonary

Multimodal medical image fusion technique is an important and essential coalescing technique for the medical images with different modalities. The fused medical image carries more useful information than two or more relevant individual medical images of modality. To enhance, preserve edge and feature information and to remove noise of the source images a novel medical image fusion method has been developed

When breast cancer is surgically removed, a rim of normal tissue surrounding the tumor is also removed. This rim of normal tissue is called a margin and is studied by a pathologist to determine whether or not all tumor was removed. The pathologist achieves this by cutting the surgically removed tissue into thin slices and observing each slice under a microscope. This process is time-consuming. Here

In order to improve the accuracy of pulmonary nodules malignancy grading, we propose a method to implement quantitative analysis for lung nodules using computed tomography (CT) signs. Firstly, we construct feature sets of CT signs by combing the radiomics features with the higher-order features extracted from a convolutional neural network. Secondly, on the basis of the mixed feature set, an evolutionary

The normal and cancer cell tissues exhibit different gene expressions. Therefore, gene expression data are the effective source for cancer classification, in which the usage of the original gene expression data is challenging due to their high dimension and small size of the data samples. This article proposes a fractional biogeography-based optimization-based deep convolutional neural network (FBBO-based

Diabetic foot ulcers (DFUs) result in amputation of lower limbs or feet without timely assessment and treatment. The assessment of DFUs is performed by the diagnosis of DFU ischaemia and infection. In this work, a new deep convolutional neural network (CNN) based approach (ResKNet) is proposed to perform such assessment. The proposed network consists of a series of unique residual blocks of 2D convolution

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID‐19) disease using normal, pneumonia, and COVID‐19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID‐19 CXRs were 4580, 3884, and 730, respectively

Brain structure segmentation, including tumors, in medical imaging has become a necessity to help neurologists correctly diagnose patients' conditions. The complexity of these structures requires the implementation of automatic segmentation methods, often developed by magnetic resonance imaging. This study aims to design and implement an automatic system for detecting and localizing tumor regions by

Medical images have become omnipresent in diagnosis and therapy. However, they can be affected by various types of noise that reduce image quality and make the final diagnostic decision difficult. The main objective of this research is to effectively remove the noise while preserving the important image characteristics. This paper proposes a novel approach for image denoising based on discrete wavelet

The shape and size of nodules are significant pointers of malignancy in cancer diagnosis. Moreover, considerable capture of nodules' structural data attained from computed tomography (CT) scans in computer‐aided design is a confronting task. Various investigations deal with computationally deep ensemble approaches/convolutional neural network (CNN) models; however, sampling tumors based on multi‐section‐You

A type of coronavirus disease called COVID‐19 is spreading all over the globe. Researchers and scientists are endeavoring to find new and effective methods to diagnose and treat this disease. This article presents an automated and fast system that identifies COVID‐19 from X‐ray radiographs of the chest using image processing and machine learning algorithms. Initially, the system extracts the feature

Novel corona virus COVID‐19 has spread rapidly all over the world. Due to increasing COVID‐19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID‐19 virus. This work offers a decision support system based on the X‐ray image to diagnose the presence of the COVID‐19 virus. A deep learning‐based

Epilepsy is one of the most common neurological diseases of the human brain. It affects the nervous system of brain which shows the impact on an individual's life because of its repetitious occurrences of seizure. Epileptic detection using automatic learning is essential to reduce the substantial work on reviewing continuous electroencephalogram (EEG) signal in spatial and temporal dimensions. A novel

Low contrast images and blurriness pose challenge in the over‐segmentation of image, which increases model complexities. In this work, a novel hybrid dermoscopic skin‐lesion segmentation method, namely SLICACO, is proposed incorporating the simple linear iterative clustering (SLIC) and ant colony optimization (ACO) algorithms. The working of proposed method is multifold. First, over‐segmentation of

Microarray technology aids in monitoring simultaneous gene expressions of human cells for diagnosis and treatment of infectious diseases in a single experiment. The microarray data are of high dimension with a large number of genes and fewer samples. Highly informative features and appropriate classifiers are necessary to increase the accuracy of disease classification. In this paper, a publicly available

Field‐emission scanning electron microscopy has become a fundamental research tool in the fields of medicine and materials science owing to its effectiveness. However, an inherent contradiction exists between the resolution of field‐emission scanning electron microscopy and its field‐of‐view. To solve this problem, we propose a deep learning‐based method for electron microscopy that can simultaneously

The feature extraction technique plays a vital role in obtaining better classification accuracy. In this paper, a novel framework is proposed, which develops two‐dimensional (2D) images for convolutional neural network (CNN) to classify four (left hand, right hand, feet, and tongue) MI tasks. 2D image is formed by decomposing each trial using continuous wavelet transform (CWT) filter bank after pre‐processing

This article develops a computer aided fully automated method for detecting and classifying the glioma brain magnetic resonance imaging (MRI) using machine learning classification approach. The noise contents in source brain MRI image are detected and removed using ridgelet filter and then the edges in noise removed image are detected using fuzzy logic and then contrast adaptive local histogram equalization

Diffuse optical tomography (DOT) is a new emerging modality in the diagnosis of soft tissue abnormalities. DOT image quality substantially depends on the reconstruction stage. In the literature, there are many reconstruction algorithms used in DOT systems. However, some algorithms were improved for solving specific cases but still need to be improved. The bi‐conjugate gradient (BiCG) enhanced is one

Blur is a key property in the perception of COVID‐19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection

In iterative reconstruction algorithms, it is very important to calculate the weighting factor quickly and accurately, which directly affects the reconstruction efficiency and accuracy of computed tomography images. In this paper, a new calculation method for accurate length weighting factor is developed. Firstly, the ray equation is determined according to the coordinates of the source and the ray's

Automated polyp localization in colon endoscopy images helps minimize human errors in localizing polyps. In our method, we use Faster Region‐based Convolutional Neural Network (R‐CNN) on Resnet 50 network to form a tight bounding box around the polyp. The bounding box is then used as input for the lazy snapping technique to determine polyps correctly. Three input variants—RGB images, histogram equalized

Magnetic resonance imaging (MRI) scan analysis is an effective tool that accurately detects abnormal brain tissue. This manuscript proposes the strategy of segmentation of brain tumors in MRI images and uses the technique of weighted fuzzy factor based on kernel metrics. Here, a deep auto encoder (DAE) with barnacle mating algorithm (BMOA) and random forest (RF) classifier are used to tumor stage classification

Accurate detection and pixel‐wise classification of brain tumors in Magnetic Resonance Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning. Manual segmentation of tumors from MRI is highly subjective and tedious. With recent advances in deep learning, automatic brain tumor segmentation is an emerging research direction in the medical imaging domain. We present

In breast cancer diagnosis, tumoral and non‐tumoral breast lesions differentiation is a challenge. We investigate the feasibility of continuous‐wave back reflection diffuse optical tomography (rDOT) in differentiation tumoral and non‐tumoral breast lesions to solve this problem. Modified Beer–Lambert Law and Rytov solution of diffusion approximation of the radiative energy transfer equation were used

Automatic lung nodules detection and segmentation can assist doctors in better diagnosis and treatment for lung cancer. However, precise detection and segmentation are still challenging, because lung nodules can have different contours or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface. In this study, an automatic detection and segmentation method

Multi‐modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography‐magnetic resonance (CT‐MR) image fusion model is proposed using an optimized bio‐inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low‐frequency) and detail (high‐frequency)

In this paper, a new mean matting method based on mean correlation is proposed to extract thin blood vessels precisely. The proposed algorithm is the combination of both supervised and unsupervised method. The supervised methodology performs well in extracting thick blood vessels; however, thin vessels are not precisely extracted. Even the capability of unsupervised method is better in extracting thin

Diabetes mellitus is a major medical concern worldwide. Long‐term diabetes can affect the retina of the eye and lead to diabetic retinopathy (DR) and diabetic macular edema (DME). Proper screening and consultation with an ophthalmologist are necessary to prevent avoidable vision loss. As DR and DME have become more prevalent, automated screening is essential to provide cost‐effective and rapid solutions

The goal of this paper is to develop an effective approach allowing to capture accurately the intrinsic nature of data using an infinite shifted‐scaled Dirichlet mixture model (InSSDMM). This article extends the finite statistical model to a more efficient multidimensional infinite mixture. The flexibility of the developed framework is demonstrated via some challenging medical applications that concern