The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

Computer-Aided Breast Cancer Detection Using Mammograms: A Review

Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, there is a high similarity between different skin lesion like melanoma and nevus, which increase the difficulty of the detection and diagnosis. A reliable automated system for skin lesion classification is essential for early detection to save effort, time and human life. In this paper, an automated skin lesion classification method is proposed. In this method, a pre-trained deep learning network and transfer learning are utilized. In addition to fine-tuning and data augmentation, the transfer learning is applied to AlexNet by replacing the last layer by a softmax to classify three different lesions (melanoma, common nevus and atypical nevus). The proposed model is trained and tested using the ph2 dataset. The well-known quantative measures, accuracy, sensitivity, specificity, and precision are used in evaluating the performance of the proposed method where the obtained values of these measures are 98.61%, 98.33%, 98.93%, and 97.73%, respectively. The performance of the proposed method is compared with the existing methods where the classification rate of the proposed method outperformed the performance of the existing methods.

Skin Cancer Classification using Deep Learning and Transfer Learning

Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other method.

Approaches For Automated Detection And Classification Of Masses In Mammograms

https://sci2s.ugr.es/keel/pdf/specific/articulo/Zhang_Neural_2004.pdf

Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection

Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses. In this study normal breast images and breast image with masses used as the standard input to the proposed system are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. In MIAS database, masses are grouped into either spiculated, circumscribed or ill-defined. Additional information includes location of masses centres and radius of masses. The extraction of the textural features of ROIs is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions for each ROI. The results show that the GLCM at 0o, 45o, 90o and 135o with a block size of 8X8 give significant texture information to identify between masses and non-masses tissues. Analysis of GLCM properties i.e. contrast, energy and homogeneity resulted in receiver operating characteristics (ROC) curve area of Az = 0.84 for Otsu's method, 0.82 for thresholding method and Az = 0.7 for K-mean clustering. ROC curve area of 0.8-0.9 is rated as good results. The authors' proposed method contains no complicated algorithm. The detection is based on a decision tree with five criterions to be analysed. This simplicity leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis system.

/pdf/identification-of-masses-in-digital-mammogram-using-gray-2h8q5syq10.pdf

Identification of masses in digital mammogram using gray level co-occurrence matrices.

Cationic polymers have been used to condense DNA by electrostatic interaction into small particles (polyplexes), for protecting the DNA from degradation and enhancing its uptake via endocytosis. Polyethylenimine (PEI) is one of the most advanced delivery systems that can condense DNA efficiently forming PEI/DNA complexes. The effect of PEI molecular weight (2k, 5k and 25k) on the physicochemical and biological properties of the polyplexes was investigated. As the molecular weight of PEI increased, the condensation ability, surface charge increased while complexes size decreased. PEI 25k has the lowest buffer capacity compared to 2k and 5k PEI. Transfection efficiency of examined polyplexes was higher in MCF7 cells than in HeLa cells. 25k PEI formed smaller polyplexes and achieved higher transfection efficiencies (into two cell lines HeLa and MCF7) than 2k PEI and 5k PEI. Attachment of different hydrophobic amino acid residues and suitable targeting ligands onto the surface of 25k PEI will increase its transfection efficiency.

Effect of polymer molecular weight on the DNA/PEI polyplexes properties

Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques.

EEG Signal Classification Using Neural Network and Support Vector Machine in Brain Computer Interface

The high incidence of breast cancer in women has increased significantly in the recent years. Mammogram -breast x-ray imaging -is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. Physician experience of detecting breast cancer can be assisted by using some computerized feature extraction algorithms. We are introducing, as an aid to radiologists, a computer diagnosis system, which could be helpful in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. The techniques used in this paper for feature extraction is based on the invariant features and fractal dimensions of locally processed image (ROI). Two statistical classifiers (The minimum distance classifier and the voting K-Nearest Neighbor classifier) were used and compared through the system to reach a better classification decision.

/pdf/computer-aided-diagnosis-of-digital-mammograms-1slvw6d4dw.pdf

Computer aided diagnosis of digital mammograms

This paper introduces an optimal solution for wavelet-based medical image fusion using different wavelet families and Principal Component Ana1ysis (PCA) based on the Modified Central Force Optimization (MCFO) technique. The main motivation of this work is to increase the quality of medical fused images in order to provide correct diagnosis of diseases for the objective of optimal therapy. This can be achieved by fusing medical images of different modalities using an optimization technique based on the MCFO. The MCFO technique gives the optimum gain parameters that achieve the best fused image quality. Histogram matching is applied to improve the overall values of the Peak Signal-to-Noise Ratio (PSNR), entropy, local contrast, and quality of the fused image. A comparative study is performed between the proposed algorithm, the traditional Discrete Wavelet Transform (DWT), and the PCA fusion using maximum fusion rule. The proposed algorithm is evaluated subjectively and objectively with different fusion quality metrics. Simulation results demonstrate that the proposed MCFO optimized wavelet-based fusion algorithm using Haar wavelet and histogram matching achieves a superior performance with the highest image quality and clearest image details in a very short processing time.

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

This paper presents a computer-aided diagnosis (CAD) system based on combined support vector machine (SVM) and linear discriminant analysis (LDA) classifier for detection and classification breast cancer in digital mammograms. The proposed system has been implemented in four stages: (a) Region of interest (ROI) selection of 32x32 pixels size which identifies suspicion regions, (b) Feature extraction stage locally processed image (ROI) to compute the important features of each breast cancer, (c) Feature selection stage by using forward stepwise linear regression method (FSLR). (d) Classification stage, which classify between normal and abnormal patterns and then classify between benign and malignant abnormal. In classification stage, a new method was used, based on combined SVM and LDA classifier (SVM/LDA), and compared to other classifiers such as SVM, LDA, and fuzzy C-mean (FCM) classifiers. The proposed system was shown to have a large potential for breast cancer diagnostic in digital mammograms.

Wael A. Mohamed

Papers

Effect of polymer molecular weight on the DNA/PEI polyplexes properties

EEG Signal Classification Using Neural Network and Support Vector Machine in Brain Computer Interface

Computer aided diagnosis of digital mammograms

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

Computer aided diagnosis in digital mammography using combined support vector machine and linear discriminant analyasis classification