SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples

Fourier transform infrared spectroscopic marker of glioblastoma ob-tained from machine learning and changes in the spectra

BACKGROUND

Glioblastoma is the most malignant brain cancer with an average survival rate of 5 years. In neurosurgical practice, it is impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue.

METHODS

Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma.

RESULTS

The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm^-1 and a shift of peaks at 1675 cm^-1 and 1637 cm^-1. Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of β-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm^-1 and 1056 cm^-1 as well as between 1564 cm^-1 and 1588 cm^-1 are characteristic of glioblastoma.

CONCLUSION

Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation.

Automatic breast lesion segmentation in phase preserved DCE-MRIs

We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.