Due to the advancements in the lifestyle, stress builds enormously among individuals. A few recent studies have indicated that stress is a major contributor for infertility and subsequent ovarian... Show More

Due to the advancements in the lifestyle, stress builds enormously among individuals. A few recent studies have indicated that stress is a major contributor for infertility and subsequent ovarian cancer among women of reproductive age. In view of this, the present study proposes a two-stage computational methodology to identify and segment the ovarian tumour and classify it as benign or malignant. Using computerized tomography images, the first stage involves image segmentation using inverted fuzzy c-Means clustering, and second stage consists of deep quantum convolutional neural network in order to detect the tumours. The efficacy of the proposed method is demonstrated using in-house clinically collected dataset by comparing the results with the state-of-the-art methods. The experimental results confirm that the proposed approach outperforms the existing fuzzy C means algorithm by achieving the average Jaccard score of (0.65, 0.84, 0.79) (min, max, avg) and Dice score of (0.70, 0.83, 0.77) (min, max, avg), classification result of 78% for benign and 70.03% for malignant tumours. The classification results using the variant of convolutional neural network (CNN) model ResNet16 are compared with the quantum convolutional neural networks (QCNN) and obtained the classification performance of 87.02% for benign and 79.4% for malignant tumours and 84.4% for benign and 77.03% for malignant tumours respectively.

Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in... Show More

Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in pyrazine compounds mined from Aspergillus flavus' final stage. This stage of flavus is detected through color change forming a pale-yellow crystal structure. Detection of the same is complex and demands an experienced fraternity to continuously monitor the growth of fungus and identify its color change. However, researches proved that the task needs to be perfect and a tiny human error leads to a catastrophe in antibiotic creation. To avoid these flaws, druggists make a huge investment on costly equipment for accurate detection. To overcome these drawbacks, this article proposes a hybrid quantum convolutional neural network that predicts various stages of the fungus from the microscope's sample. To train the network, about 47,000 samples were poised under typical lab settings. The proposed system was tested in usual conditions and positively isolated the mature samples with 96% efficiency.