Computer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities. This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features. In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is 93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN.

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Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

The most common endocrine cancer is thyroid cancer. The incidental rate of thyroid cancer has significantly increased during the past few decades. Timely identification and suitable treatment are essential for better outcome. High-resolution ultrasound is the preferred modality for the detection of thyroid nodules as it has the capability of locating tiny nodules. This article proposes a feature extraction method by integrating steerable pyramid decomposition and cooccurrence matrix features for the characterization of the thyroid nodule. Steerable pyramid decomposition is carried out both in time domain and frequency domain. Textural features are obtained from the pyramid at different levels and with different filters. ReliefF method is used for feature selection. Support vector machine is used to classify the thyroid nodule as benign or malignant, and its performance is evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false alarm rate, and F1_score. The proposed approaches are tested on a dataset containing 110 thyroid ultrasound images (benign, malignant, and borderline cases). A very high overall accuracy of 99.08% with 100% sensitivity (malignant nodule detected as malignant) and 98.16% specificity (benign nodule detected as benign) is obtained for features extracted from steerable pyramid coefficients through convolution using sp1 filter at level 3. Experimental results clearly indicate that steerable pyramid–based cooccurrence matrix features can effectively describe the distinctive nature of the thyroid nodule in ultrasound image.