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Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data. [ABSTRACT FROM AUTHOR]

Present study focused on the factors affecting citrus growers’ perception regarding production and protection problems in Tehsil Toba Tek Singh, Pakistan. Study was conducted during 2019. For the collection of data from the selected 120 respondents, a well-planned interview schedule was prepared. The observation technique was also used to conduct a qualitative in-depth investigation. For drawing conclusions and making suggestions, the data was analyzed using the statistical package for social sciences (SPSS). It was found that adoption of intercropping, crop rotation, adequate extension services and costly labor were the major problem to control insect pest and diseases. But, cost of pesticides spraying, non-availability of machinery, lack of adoption of recommendation and judicious use of pesticides were the major hurdles regarding to chemical control of insect pest and diseases. Analysis showed that citrus growers’ education and income negatively associated with their protection and protection problems. But, size of land holding and farm size (citrus) positively associated with their protection and protection problems. It was also observed that lack of technical knowledge was an issue faced by the citrus growers. It was recommended, extension workers should arrange training workshop for citrus growers at village/union council level. [ABSTRACT FROM AUTHOR]