FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI.

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.

Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis.

Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection.

Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)-CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.

Green Synthesis of Gold and Silver Nanoparticles by Using Amorphophallus paeoniifolius Tuber Extract and Evaluation of Their Antibacterial Activity.

In this report, we discussed rapid, facile one-pot green synthesis of gold and silver nanoparticles (AuNPs and AgNPs) by using tuber extract of Amorphophallus paeoniifolius, and evaluated their antibacterial activity. AuNPs and AgNPs were synthesized by mixing their respective precursors (AgNO3 and HAuCl4) with tuber extract of Amorphophallus paeoniifolius as the bio-reducing agent. Characterization of AuNPs and AgNPs were confirmed by applying UV-vis spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDS). From UV-vis characterization, surface plasmon resonance spectra were found at 530 nm for AuNPs and 446 nm for AgNPs. XRD data confirmed that both synthesized nanoparticles were face-centered cubic in crystalline nature, and the average crystallite sizes for the assign peaks were 13.3 nm for AuNPs and 22.48 nm for AgNPs. FTIR data evaluated the characteristic peaks of different phytochemical components of tuber extract, which acted as the reducing agent, and possibly as stabilizing agents. The antibacterial activity of synthesized AuNPs and AgNPs were examined in Muller Hinton agar, against two Gram-positive and four Gram-negative bacteria through the disc diffusion method. AuNPs did not show any inhibitory effect, while AgNPs showed good inhibitory effect against both Gram-positive and Gram-negative bacteria.

Molecular Detection of Brucella spp. from Milk of Seronegative Cows from Some Selected Area in Bangladesh.

Brucellosis is endemic in Bangladesh both in humans and in animals. A number of reasons complicate the diagnosis, as bovine brucellosis can be diagnosed by various serological tests. But the tests have a limitation; when the organism remains intracellular, the disease goes into chronic stage and the antibody titres may decline. The present study was conducted for isolation and detection of Brucella spp. by polymerase chain reaction (PCR) from seronegative cows. A total of 360 dairy cows from three geographical regions were screened serologically by Rose Bengal Plate Test (RBPT) where 24 samples were serologically positive and the rest of the samples were serologically negative. Among the 24 seropositive individuals, 11 were culture positive and 6 were culture positive from serologically negative dairy cows. The overall seroprevalence of brucellosis in cattle was 6.6% and in disease condition a higher prevalence was recorded in abortion (28.07%) followed by infertility (13.33%). To confirm the Brucella spp. in seronegative dairy cattle, the isolates were extracted and PCR was conducted, which produced 905 bp amplicon size of 6 Brucella spp. from milk sample. So, for the detection or eradication of brucellosis, a bacteriological test and a PCR technique should be performed with the serological test of milk.