The listening to the sounds of lungs is very important to know the detection and analysis of respiratory disorders. Physicians are not able to detect accurately lung sounds of patients. Many computer programs are conducted to help physicians in diagnosing lung diseases. In this paper, a robust classification method of lung sounds (i.e. polyphonic or stridor) is proposed Features are extracted using Discrete Wavelet Transform (DWT) first. Secondly, linear prediction cepstral coe f ficients (LPCCs) are calculated After that delta and delta-delta of LPCCs are extracted Variance and kurtosis of LPCCs, delta LPCCs and delta-delta LPCCs are extracted as features of lung sounds. Classification of lung sounds is conducted using support vector machine (SVM). Training and testing data are chosen randomly from 42 subjects using cross validation. Both numbers of testing and training subjects are 21. The obtained recognition percent is 95.24%' So, new classification algorithm is conducted between polyphonic and stridor sounds of lung sounds. The obtained recognition percent is the most

BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε´) and conductivity (σ). OBJECTIVE: To examine if the dielectric behavior of normal and malignant tissues at low frequencies (α dispersion) will lead to the threshold (separating) line between them and find the threshold values of capacitance and resistance. These data are used as input for deep learning neural networks, and the outcomes are normal or malignant. METHODS: ε´ and σ in the range of 50 Hz to 100 KHz for 15 human malignant tissues and their corresponding normal ones have been measured. The separating line equation between the two classes is found by mathematical calculations and verified via support vector machine (SVM). Normal range and the threshold value of both normal capacitance and resistance are calculated. RESULTS: Deep learning analysis has an accuracy of 91.7%, 85.7% sensitivity, and 100% specificity for instant and automatic prediction of the type of breast tissue, either normal or malignant. CONCLUSIONS: These data can be used in both cancer diagnosis and prognosis follow-up.

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