A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Fusion of Local and Global Feature Representation With Sparse Autoencoder for Improved Melanoma Classification.

Automated skin cancer diagnosis is challenging due to inter-class uniformity, intra-class variation, and the complex structure of dermoscopy images. Convolutional neural networks (CNN) have recently made considerable progress in melanoma classification, even in the presence of limited skin images. One of the drawbacks of these methods is the loss of image details caused by downsampling high-resolution skin images to a low resolution. Further, most approaches extract features only from the whole skin image. This paper proposes an ensemble feature fusion and sparse autoencoder (SAE) based framework to overcome the above issues and improve melanoma classification performance. The proposed method extracts features from two streams, local and global, using a pre-trained CNN model. The local stream extracts features from image patches, while the global stream derives features from the whole skin image, preserving both local and global representation. The features are then fused, and an SAE framework is subsequently designed to enrich the feature representation further. The proposed method is validated on ISIC 2016 dataset and the experimental results indicate the superiority of the proposed approach.

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3 ×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19.

An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.

AIM: : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. METHODS: : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. RESULTS: : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. CONCLUSIONS: : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.

The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.

Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities.

Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great clinical significance. Hence, it has recently become an active area of research in biomedical image processing. In this paper, an automated multiclass brain MR classification framework is proposed to categorize the MR images into five classes such as brain stroke, degenerative disease, infectious disease, brain tumor, and normal brain. A texture based feature descriptor is proposed using curvelet transform and Tsallis entropy to extract salient features from MR images. The potential of Tsallis entropy features is compared with Shannon entropy features. A kernel extension of random vector functional link network (KRVFL) is used to perform multiclass classification and improve the generalization performance at faster training speed. To validate the proposed method, two standard multiclass brain MR datasets (MD-1 and MD-2) are used. The proposed system obtained classification accuracies of 97.33% and 94.00% for MD-1 and MD-2 datasets respectively using 5-fold cross validation approach. The experimental results demonstrated the effectiveness of our system compared to the state-of-the-art schemes and hence, can be utilized as a supportive tool by physicians to verify their screening.

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level- 2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset- 255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested scheme offers better performance than other existing schemes in terms of accuracy and number of features. The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset- 255, an accuracy of 99.45% is achieved.

Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy.

AIM: Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. MATERIALS: T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). METHOD: We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier. RESULTS: The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively. CONCLUSION: Our developed system is promising and effective in detecting hearing loss.

Widely infiltrating epithelioid malignant peripheral nerve sheath tumour of skull base.

The epithelioid variant of malignant peripheral nerve sheath tumours is a rare histological entity, and the occurrence of a malignant peripheral nerve sheath tumour in the skull base is even more unusual. We report a case of a 52-year-old man who presented with reduced hearing in the left ear, giddiness and left-sided facial weakness of short duration. He was a known hypertensive. On examination, left-sided 7th to 12th cranial nerve palsies were noted. Computed tomography (CT) and brain magnetic resonance imaging (MRI) were reported as an ill-defined heterogeneously enhancing mass left skull base suggestive of chondrosarcoma. Left tympanotomy and biopsy of the lesion were carried out. On light microscopy and immunohistochemical examination of the biopsy, a diagnosis of epithelioid malignant peripheral nerve sheath tumour was established. The patient underwent left extended modified radical mastoidectomy and selective neck dissection. Histopathological study of the resected surgical specimen confirmed left-sided extensive tumour involvement of skull base structures, as well as neck nodal metastases.

Low-grade adenocarcinoma of endolymphatic sac origin.

We present a case of papillary cystic low-grade adenocarcinoma of endolymphatic sac origin. These tumors are very rare and only a few cases have been reported in literature. They have a protracted clinical course causing progressive invasion and bony destruction. Radiologically, they appear as a heterogenous lobulated vascular mass, invading bone and compressing surrounding structures. Histologically, these tumors are composed of uniform population of cells, resembling normal endolymphatic sac epithelium. This causes considerable diagnostic difficulty. A strong index of suspicion along with clinical and radiological correlation is essential to arrive at a correct diagnosis. In some bilateral cases, association with von Hippel-Lindau disease has been noted. Radical mastoidectomy and temporal bone resection, which may sometimes necessitate sacrifice of cranial nerves, is the treatment of choice.

KTP-532 laser cordotomy for bilateral abductor paralysis.

Various lateralization procedures have been described in the past to treat bilateral vocal cord paralysis. Though endoscopie lateralization gives good results in terms ofdecannulation rates, the postoperative voice quality is often poor. KTP-532 laser assisted posterior cordotomy was done in 3 cases. This preliminary study showd 100% decannulation rate and good post-operative voice quality. The latter was assessed both subjectively and objectively on VAGMI scales.