V. Swetha^*, G. Vadivu

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 707-726, 2023, DOI:10.32604/iasc.2023.035483

Abstract Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified… More >

P. Gomathi^1,*, C. Muniraj², P. S. Periasamy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808

Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary to focus on the four… More >

Prasanalakshmi Balaji^1,*, B. Sri Revathi², Praveetha Gobinathan³, Shermin Shamsudheen³, Thavavel Vaiyapuri⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560

Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the most interesting research subjects in… More >

M. N. Kavitha^1,*, A. RajivKannan²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 689-704, 2023, DOI:10.32604/iasc.2023.025437

Abstract Facial Expression Recognition (FER) has been an important field of research for several decades. Extraction of emotional characteristics is crucial to FERs, but is complex to process as they have significant intra-class variances. Facial characteristics have not been completely explored in static pictures. Previous studies used Convolution Neural Networks (CNNs) based on transfer learning and hyperparameter optimizations for static facial emotional recognitions. Particle Swarm Optimizations (PSOs) have also been used for tuning hyperparameters. However, these methods achieve about 92 percent in terms of accuracy. The existing algorithms have issues with FER accuracy and precision. Hence, the overall FER performance is… More >

M. Carmel Sobia¹, A. Abudhahir²

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 869-881, 2018, DOI:10.31209/2018.100000014

Abstract In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB. More >