This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.

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An intelligent edge detection method is proposed. The method is based on the use of pattern recognition and machine learning techniques to combine the outputs of multiple edge detection algorithms. In this way, the limitations of the individual edge detectors can be overcome and performance enhancement is achieved. Two widely used classification algorithms, the Naive Bayes Classifier and the Multi-layer Perceptron, were selected for the learning task. The proposed system was evaluated on artificial and real images. A simple class labeling system based on the output of all edge detectors is suggested to provide controllability between detection sensitivity and noise resistance. Principal Component Analysis was performed to reduce computational burden and improve detection accuracy. The method is shown to achieve a practical compromise between detection sensitivity, computational complexity, and noise immunity.

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Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders in premenopausal women. PCOS impacts women of reproductive age regardless of ethnic origin, although the signs and symptoms may vary by ethnic group. Symptoms include obesity, hirsutism, acne, amenorrhea, sterility, occasional menometrorrhagia. The objectives of thi study is to assess the possible association of vitamin D3 with BMI in PCOS and to investigate the role of serum leptin and vitamin D3 levels in the pathogenesis of PCOS. To shed light on the pathophysiology of PCOS. A case-control study was performed in Al- Batool Teaching Hospital in Baquba city during the period from 1st December 2020 to the end of March 2021. It included 50 PCOS patients and 34 subjects as healthy control. The biomarkers studied were: serum Vitamin D3, Leptin, LH and FSH and then Serum Vitamin D3 and Leptin were measured by ELISA technique. But serum LH and FSH were measured by Cobas e 411 system. Serum Vitamin D3 levels have decreased significantly and FSH in the patients’ group as compared to the control group, while for the other variables, the mean values of leptin, LH, LH/FSH, & BMI were significantly more significant in the PCOS group than in the control group. In conclusion, women who suffer from polycystic ovarian syndrome PCOS were more prone to lack vitamin D and FSH levels than those without PCOS. At the same time, there is higher LH, LH/FSH, and BMI levels for PCOS patients from healthy women. The PCOS group had higher blood leptin concentrations, especially in overweight and obese patients; because leptin is produced from adipose tissue, obesity seems to exacerbate blood higher leptin in PCOS patients, significantly impairing reproductive functions. A significant negative correlation was found between serum vitamin D3 levels and BMI in the PCOS group; as a result, obesity contributes to vitamin D deficiency risk.