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.

Conformation and stability of elastase from Atlantic cod, Gadus morhua.

Metal binding and conformational stability characteristics of psychrophilic elastase (ACE) from Atlantic cod (Gadus morhua) has been investigated. Chelation to Ca(2+) was found to be important for maintaining the biologically active conformation and for the thermal stability of the enzyme. However, presence of metal ions such as Zn(2+), Fe(3+) and Cu(2+) was found to inhibit its hydrolytic activity and so did the chelating agent EDTA. Both pH and guanidinium chloride induced denaturation of the enzyme was followed by monitoring the changes in the tryptophan fluorescence. ACE exhibited a simple two-state unfolding pattern in both acidic and basic conditions with the midpoint of transition at pH values 4.08 and 10.29, respectively. Guanidinium chloride and heat induced denaturation of the enzyme was investigated at two pH values, 5.50 and 8.00, wherein the enzyme possesses similar tertiary structure but differ in its hydrolytic activity. Guanidinium chloride induced denaturation indicated that the enzyme unfolds with a C(m) of 1.53 M at pH 8.0 and a DeltaG(H2O) of 6.91 kJ mol(-1) (28.65 J mol(-1) residue(-1)) which is the lowest reported for psychrophilic enzymes investigated till-date. However, at pH 5.50, DeltaG(H2O) value is slightly lowered by 0.65 kJ mol(-1) consistent with the observed increase in the apparent quenching constant obtained with acrylamide. On the other hand, increase in T(m) by 38.45 degrees C was observed for the enzyme at acid pH (5.50) in comparison to the heat induced unfolding at pH 8.0. The increase in the apparent T(m) has been attributed to the possible weak intermolecular association of the enzyme molecules at moderately high temperatures that is favoured by the increase in the accessible surface area / dynamics under acidic conditions. The stability characteristics of ACE have been compared with the available data for mesophilic porcine pancreatic elastase and possible mechanism for the low temperature adaptation of ACE has been proposed.

Enhanced catalytic and conformational stability of Atlantic cod trypsin upon neoglycosylation.

The applicability of psychrophilic enzymes is limited because of their lower thermodynamic stability in spite of their higher catalytic rate. In this study, we have shown that the thermodynamic stability of the psychrophilic Atlantic cod trypsin could be enhanced appreciably by covalent chemical modification with oxidized sucrose polymer without affecting its hydrolytic activity. The acquired stability of cod trypsin was found to be on par with the mesophilic porcine trypsin.

Serum copper, ceruloplasmin, protein thiols and thiobarbituric acid reactive substance status in liver cancer associated with elevated levels of alpha-fetoprotein.

Serum copper, ceruloplasmin, protein thiols and thiobarbituric acid reactive substances (TEARS) were estimated in 25 patients of liver cancer. The copper to ceruloplasmin ratio was moderately increased (P<0.05) but the copper (P<0.001) and ceruloplasmin (P<0.001) levels were significantly increased in liver cancer patients when compared to controls. Protein thiols levels were found to be highly significant (P<0.001). Where as the TEARS levels were not found to be significant. Trace elements and free radicals have been implicated in the etiology of cancer. Hence the estimation of ceruloplasmin and protein thiols along with the copper may be of high value in the early diagnosis of cancer.

Serum copper, ceruloplasmin and thiobarbituric acid reactive substance status in patients with ovarian cancer.

Serum copper, ceruloplasmin and thiobarbituric acid reactive substances (TBARS) were estimated in 30 patients of ovarian cancer. The copper to ceruloplasmin ratio was moderately increased (P<0.05) but the copper (P<0.01) and ceruloplasmin (P<0.001) levels were significantly increased in ovarian cancer patients as compared to controls. TBARS levels were also found to be highly significant (P<0.001). Trace elements and free radicals have been implicated in the etiology of cancer. Hence the estimation of antioxidants like ceruloplasmin and TBARS along with the trace element like copper may be of value in the early diagnosis of cancer.

Serum copper, ceruloplasmin and thiobarbituric acid reactive substance status in patients with ovarian cancer.

Serum copper, ceruloplasmin and thiobarbituric acid reactive substances (TBARS) were estimated in 30 patients of ovarian cancer. The copper to ceruloplasmin ratio was moderately increased (P<0.05) but the copper (P<0.01( and ceruloplasmin (P<0.001) levels were significantly increased in ovarian cancer patients as compared to controls. TBARS levels were also found to be highly significant (P<0.001). Trace elements and free radicals have been implicated in the etiology of cancer. Hence the estimation of antioxidants like ceruloplasmin and TBARS along with the trace element like copper may be of value in the early diagnosis of cancer.