Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer.

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC causes maximum deaths in women. In this work, three deep learning (DL) models such as Vision Transformer (ViT), Convmixer, and Visual Geometry Group-19 (VGG-19) are implemented for the detection and classification of different breast cancer tumors with the help of Breast cancer histopathological (Break His) image database. The performance of each model is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1-score, and area under the curve (AUC). From the simulation result, ViT showed the best performance for binary classification of breast cancer tumors with accuracy, precision, recall, and F1-score of 99.89 %, 98.29 %, 98.29 %, and 98.29 %, respectively. Also, ViT showed the best performance in terms of accuracy (98.21 %), average Precision (89.84 %), recall (89.97 %), and F1-score (88.75) for eight class classifications. Moreover, we have also ensemble the ViT-Convmixer model and observed that the performance of the ensemble model is reduced as compared to the ViT model. We have also compared the performance of the proposed best model with other existing models reported by several research groups. The study will help find suitable models that will increase accuracy in early diagnoses of BC. We hope the study will also help to minimize human errors in the early diagnosis of this fatal disease and administer appropriate treatment. The proposed model may also be implemented for the detection of other diseases with improved accuracy.

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification.

The world faces multiple public health emergencies simultaneously, such as COVID-19 and Monkeypox (mpox). mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.

Risk prediction of cardiovascular disease using machine learning classifiers.

Cardiovascular disease (CVD) makes our heart and blood vessels dysfunctional and often leads to death or physical paralysis. Therefore, early and automatic detection of CVD can save many human lives. Multiple investigations have been carried out to achieve this objective, but there is still room for improvement in performance and reliability. This study is yet another step in this direction. In this study, two reliable machine learning techniques, multi-layer perceptron (MLP), and K-nearest neighbour (K-NN) have been employed for CVD detection using publicly available University of California Irvine repository data. The performances of the models are optimally increased by removing outliers and attributes having null values. Experimental-based results demonstrate that a higher accuracy in detection of 82.47% and an area-under-the-curve value of 86.41% are obtained using the MLP model, unlike the K-NN model. Therefore, the proposed MLP model was recommended for automatic CVD detection. The proposed methodology can also be employed in detecting other diseases. In addition, the performance of the proposed model can be assessed via other standard data sets.

Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.

Objective: Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods: Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results: From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion: The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.

The tobacco MAP215/Dis1-family protein TMBP200 is required for the functional organization of microtubule arrays during male germline establishment.

The haploid microspore division during pollen development in flowering plants is an intrinsically asymmetric division which establishes the male germline for sexual reproduction. Arabidopsis gem1 mutants lack the male germline as a result of disturbed microspore polarity, division asymmetry, and cytokinesis and represent loss-of-function mutants in MOR1/GEM1, a plant orthologue of the conserved MAP215/Dis1 microtubule associated protein (MAP) family. This provides genetic evidence for the role of MAP215/Dis1 in the organization of gametophytic microtubule arrays, but it has remained unknown how microtubule arrays are affected in gem1 mutant microspores. Here, novel male gametophytic microtubule-reporter Nicotiana tabacum plants were constructed, expressing a green fluorescent protein-alpha-TUBULIN fusion protein (GFP-TUA6) under the control of a microspore-specific promoter. These plants allow effective visualization of all major male gametophytic microtubule arrays and provide useful tools to study the regulation of microtubule arrays by MAPs and other effectors. Depletion of TMBP200, a tobacco homologue of MOR1/GEM1 in gametophytic microtubule-reporter plants using microspore-targeted RNA interference, induced defects in microspore polarity, division asymmetry and cytokinesis that were associated with striking defects in phragmoplast position, orientation, and structure. Our observations further reveal a requirement for TMBP200 in gametophytic spindle organization and a novel role in spindle position and orientation in polarized microspores. These results provide direct evidence for the function of MAP215/Dis1 family protein TMBP200 in the organization of microtubule arrays critical for male germline formation in plants.

Arabidopsis kinesins HINKEL and TETRASPORE act redundantly to control cell plate expansion during cytokinesis in the male gametophyte.

Asymmetric cell division at pollen mitosis I (PMI) is required to specify the differential fate of the daughter vegetative and generative cells. Cytokinesis at PMI displays specialized features, and it has been suggested that there might be distinct molecular pathways underpinning different modes of cytokinesis in plants. Activation of the NACK-PQR MAP kinase signaling pathway, which is essential for somatic cell cytokinesis in tobacco, depends upon the NACK1 and NACK2 kinesin-related proteins. Their Arabidopsis orthologs, HINKEL (HIK) and TETRASPORE (TES), were reported to be essential for cytokinesis in somatic cells and in microsporocytes, respectively. More recently, HIK and TES were shown to have a functionally redundant role in female gametophytic cytokinesis. We report here that HIK and TES are co-expressed in microspores and developing pollen, and, through analysis of microspore and pollen development in double heterozygote mutants, the occurrence of cell plate expansion defects during cytokinesis at PMI. The data demonstrate a functionally redundant role for HIK and TES in cell plate expansion during male gametophytic cytokinesis, extending the concept that different modes of cytokinesis are executed by a common signaling pathway, but reinforcing the individuality of gametophytic cytokinesis in its requirement for either TES or HIK.