Background Skin cancer is one of the life threating diseases in the world. So, millions of lives can be saved by early detection of skin cancer. In addition, automating the computer‐aided system of skin lesion detection and classification (SLDC) will assist the medical practitioners to ensure more efficacious treatment of skin lesion disease. Material and Method In this article, a hybrid preprocessing‐based transfer learning model for SLDC is proposed, which is named as SLDCNet. Initially, the hybrid Gaussian filter (HGF) with connected component label (CCL) based fast march inpainting procedure is used for hair removal and denoising of skin lesions. Next, full resolution convolutional networks (FrCN) based segmentation method is adapted for detecting the cancer region. Then, feature extraction is performed using deep residual learning and finally, transfer learning mechanism is applied for classification of eight skin lesions. Results The extensive simulation results shows that proposed SLDCNet resulted in a classification accuracy of 99.92%, sensitivity of 99%, and specificity of 99.36%, respectively. Conclusion From the obtained results, it is proven that proposed SLDCNet provides better performance as compared to state‐of‐art SLDC approaches, and even the standard ISIC‐2019 public challenge.

Breast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whereas at advanced stages chances of survival are bleak. Screening programs aid in detecting potential breast cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening breast cancer, because even small tumors can be detected on mammograms. In this study, a novel feature extraction technique based on dyadic wavelet transform for classification of microcalcification in digital mammograms is proposed. In the feature extraction module, the high frequency sub-bands obtained from the decomposition of dyadic wavelet transform is used to form innovative sub-bands. From the newly constructed sub-bands, the features such as energy and entropy are computed. In the classification module, the extracted features are fed into a Gaussian Mixture Model (GMM) classifier and the severity of given microcalcification; benign or malignant are given. A classification accuracy of 95.5% is obtained using the proposed approach on DDSM database.

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Speech enhancement techniques are very important in the field of signal processing for their numerous applications. They are employed in many contexts such as hands-free telephony, hearing aid systems, re-mastering of audio recordings, preprocessing for speech recognition interfaces, etc. In this paper, an efficient cascade combination approach for cancellation of noises in speech signals is discussed. It is possible to obtain higher performance by cascading two or more algorithms. A better noise removal approach is presented by cascading wavelet and adaptive filters. Results show that the cascade approach gives high Peak Signal to Noise Ratio (PSNR) and low Root Mean Square Error (RMSE) than individual performances of wavelet and an adaptive filter.