Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.

Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient's treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and F1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, F1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results.

An Efficient Cancer Classification Model for CT/MRI/PET Fused Images.

OBJECTIVE: The aim was to study image fusion-based cancer classification models used to diagnose cancer and assess medical problems in earlier stages that help doctors or health care professionals to make the treatment plan accordingly. METHODS: In this work, a novel image fusion method based on Curvelet transform is developed. CT and PET scan images of benign type tumors were fused together using the proposed fusion algorithm and the same way, MRI and PET scan images of malignant type tumors were fused together to achieve the combined benefits of individual imaging techniques. Then, the marker-controlled watershed algorithm was applied on fused images to segment cancer affected area. The various color features, shape features and texture-based features were extracted from the segmented image. Following this, a data set was formed with various features, given as input to different classifiers namely neural network classifier, Random forest classifier, and K-NN classifier to determine the nature of cancer. The results of the classifier showed normal, benign or malignant category of cancer. RESULTS: The performance of the proposed fusion algorithm was compared with the existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of the classifiers were evaluated using three parameters: accuracy, sensitivity, and specificity. The K-NN Classifier performed better compared to the other two classifiers and it provided an overall accuracy of 94%, sensitivity of 88% and specificity of 84%. CONCLUSION: The proposed Curvelet transform based image fusion method combined with the KNN classifier provides better results compared to other two classifiers when two input images were used individually.

A case of human leukocyte antigen B-27-associated ocular hypotony successfully treated with golimumab.

A 42-year-old male presented to us after an episode of acute anterior human leukocyte antigen (HLA)-B27-associated uveitis, and intraocular pressure (IOP) in the right eye was 4 mmHg. Ultrasound biomicroscopy revealed ciliary body edema with supraciliary effusion. He was on a frequent topical corticosteroid, and oral steroid in addition to receiving a periocular injection depot corticosteroid 20 days back. He was started on treatment with subcutaneous golimumab (GLM). After a month, his IOP in the right eye was 14 mm of Hg with UBM showing resolution of ciliary body edema. GLM can be useful in the management of steroid-resistant cases of HLA B-27-associated ocular hypotony.

Protocols for Improvement of Black Pepper (Piper nigrum L.) Utilizing Biotechnological Tools.

Black pepper, Piper nigrum L., the "King of spices" is the most widely used spice growing in the South-Western region of India. The humid tropical evergreen forest bordering the Malabar Coast (Western Ghats is one of the hot spot areas of plant bio-diversity on earth) is its center of origin and diversity. However, the crop faces constraints like rampant fungal and viral diseases, lack of disease free planting material, hence biotechnological tools can be utilized to address these problems and strides have been made successfully. The standardization of micropropagation, somatic embryogenesis, in vitro conservation, protoplast isolation, and genetic transformation protocols are described here. The protocols could be utilized to achieve similar goals in the related species of Piper too.

Cryopreservation of in vitro grown shoots of ginger (Zingiber officinale Rosc.).

An efficient cryopreservation technique for in vitro grown shoots of ginger (Zingiber officinale Rosc) was developed based on encapsulation dehydration, encapsulation vitrification and vitrification procedures. Pregrowth and serial preculture were needed to obtain the best regrowth for all techniques. The vitrification procedure resulted in higher regrowth (80%) when compared to encapsulation vitrification (66%) and encapsulation dehydration (41%). In the vitrification procedure shoots were: precultured in liquid Murashige-Skoog medium containing 0.3 M sucrose for 3 days; cryoprotected with a mixture of 5% DMSO and 5% glycerol for 20 min at room temperature; osmoprotected with a mixture of 2 M glycerol and 0.4 m sucrose for 20 min at 25 degrees C; before being dehydrated with a highly concentrated vitrification solution (PVS2) for 40 min at 25 degrees C. The dehydrated shoots were transferred to 2 ml cryotubes, suspended in 1 ml PVS2 and plunged directly into liquid nitrogen. In all the three cryopreservation procedures tested, shoots grew from cryopreserved shoot tips without intermediary callus formation. The genetic stability of cryopreserved ginger shoot buds were confirmed using ISSR and RAPD profiling.