A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification.

There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm.

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review.

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets.

Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. These features are locked with data increasingly being generated from different sources such as social media, surveillance systems, network applications, and medical records. The high dimensionality of these datasets often impairs the quality of the optimal combination of these features selected. The use of the binary optimization method has been proposed in the literature to address this challenge. However, the underlying deficiency of the single binary optimizer is transferred to the quality of the features selected. Though hybrid methods have been proposed, most still suffer from the inherited design limitation of the single combined methods. To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. The approach used in this study designed a sub-population selective mechanism that dynamically assigns individuals to a 2-level optimization process. The level-1 method first mutates items in the population and then reassigns them to a level-2 optimizer. The selective mechanism determines what sub-population is assigned for the level-2 optimizer based on the exploration and exploitation phase of the level-1 optimizer. In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. The outcome of these are the HBEOSA-SA and HBEOSA-FFA, which are then investigated on the NT, and their corresponding variants HBEOSA-SA-NT and HBEOSA-FFA-NT with no NT applied. The hybrid methods were experimentally tested over high-dimensional datasets to address the challenge of feature selection. A comparative analysis was done on the methods to obtain performance variability with the low-dimensional datasets. Results obtained for classification accuracy for large, medium, and small-scale datasets are 0.995 using HBEOSA-FFA, 0.967 using HBEOSA-FFA-NT, and 0.953 using HBEOSA-FFA, respectively. Fitness and cost values relative to large, medium, and small-scale datasets are 0.066 and 0.934 using HBEOSA-FFA, 0.068 and 0.932 using HBEOSA-FFA, with 0.222 and 0.970 using HBEOSA-SA-NT, respectively. Findings from the study indicate that the HBEOSA-SA, HBEOSA-FFA, HBEOSA-SA-NT and HBEOSA-FFA-NT outperformed the BEOSA.

Advanced dwarf mongoose optimization for solving CEC 2011 and CEC 2017 benchmark problems.

This paper proposes an improvement to the dwarf mongoose optimization (DMO) algorithm called the advanced dwarf mongoose optimization (ADMO) algorithm. The improvement goal is to solve the low convergence rate limitation of the DMO. This situation arises when the initial solutions are close to the optimal global solution; the subsequent value of the alpha must be small for the DMO to converge towards a better solution. The proposed improvement incorporates other social behavior of the dwarf mongoose, namely, the predation and mound protection and the reproductive and group splitting behavior to enhance the exploration and exploitation ability of the DMO. The ADMO also modifies the lifestyle of the alpha and subordinate group and the foraging and seminomadic behavior of the DMO. The proposed ADMO was used to solve the congress on evolutionary computation (CEC) 2011 and 2017 benchmark functions, consisting of 30 classical and hybrid composite problems and 22 real-world optimization problems. The performance of the ADMO, using different performance metrics and statistical analysis, is compared with the DMO and seven other existing algorithms. In most cases, the results show that solutions achieved by the ADMO are better than the solution obtained by the existing algorithms.

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models.

Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets.

The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.

Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images.

The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architectures is proof of the outstanding performance reported in the literature. However, localizing abnormalities in regions of images that can support the confidence of classification continues to attract research interest. The difficulty of using digital histopathology images for this task is another drawback, which needs high-level deep learning models to address the situation. Successful pathology localization automation will support automatic acquisition planning and post-imaging analysis. In this paper, we address issues related to the combination of classification with image localization and detection through a dual branch deep learning framework that uses two different configurations of convolutional neural networks (CNN) architectures. Whole-image based CNN (WCNN) and region-based CNN (RCNN) architectures are systematically combined to classify and localize abnormalities in samples. A multi-class classification and localization of abnormalities are achieved using the method with no annotation-dependent images. In addition, seamless confidence and explanation mechanism is provided so that outcomes from WCNN and RCNN are mapped together for further analysis. Using images from both BACH and BreakHis databases, an exhaustive set of experiments was carried out to validate the performance of the proposed method in achieving classification and localization simultaneously. Obtained results showed that the system achieved a classification accuracy of 97.08%, a localization accuracy of 94%, and an area under the curve (AUC) of 0.10 for classification. Further findings from this study revealed that a multi-neural network approach could provide a suitable method for addressing the combinatorial problem of classification and localization anomalies in digital medical images. Lastly, the study's outcome offers means for automating the annotation of histopathology images and the support for human pathologists in locating abnormalities.

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.

Research in deep learning (DL) has continued to provide significant solutions to the challenges of detecting breast cancer in digital images. Image preprocessing methods and architecture enhancement techniques have been proposed to improve the performance of DL models such as convolutional neural networks (CNNs). For instance, the wavelet decomposition function has been used for image feature extraction in CNNs due to its strong compactness. Additionally, CNN architectures have been optimized to improve the process of feature detection to support the classification process. However, these approaches still lack completeness, as no mechanism exists to discriminate features to be enhanced and features to be eliminated for feature enhancement. More so, no studies have approached the use of wavelet transform to restructure CNN architectures to improve the detection of discriminant features in digital mammography for increased classification accuracy. Therefore, this study addresses these problems through wavelet-CNN-wavelet architecture. The approach presented in this paper combines seam carving and wavelet decomposition algorithms for image preprocessing to find discriminative features. These features are passed as input to a CNN-wavelet structure that uses the new wavelet transformation function proposed in this paper. The CNN-wavelet architecture applied layers of wavelet transform and reduced feature maps to obtain features suggestive of abnormalities that support the classification process. Meanwhile, we synthesized image samples with architectural distortion using a generative adversarial network (GAN) model to argue for their training datasets' insufficiency. Experimentation of the proposed method was carried out using DDSM + CBIS and MIAS datasets. The results obtained showed that the new method improved the classification accuracy and lowered the loss function values. The study's findings demonstrate the usefulness of the wavelet transform function in restructuring CNN architectures for performance enhancement in detecting abnormalities leading to breast cancer in digital mammography.

A generative adversarial network for synthetization of regions of interest based on digital mammograms.

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.

Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies.

The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images.

The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.

A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

A case-based reasoning framework for early detection and diagnosis of novel coronavirus.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 11,301,850 confirmed cases while more than 531,806 have died due to it, with these figures rising daily across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is, therefore, crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is a compelling paradigm that allows for the utilization of case-specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study, therefore, aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in the classification of suspected cases of COVID-19. The CBR model leverages on a novel feature selection and the semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved from 71 (67 adults and 4 pediatrics) cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories with an accuracy of 94.54%. The study found that the proposed model can support physicians to easily diagnose suspected cases of COVID-19 based on their medical records without subjecting the specimen to laboratory tests. As a result, there will be a global minimization of contagion rate occasioned by slow testing and in addition, reduced false-positive rates of diagnosed cases as observed in some parts of the globe.