Efficient Guided Grad-CAM Tuned Patch Neural Network for Accurate Anomaly Detection in Full Images


  • R. Rajkumar Dept. of CSE, Chettinad College of Engineering & Technology, Karur - 639114, Tamil Nadu, India
  • D. Shanthi Dept. of CSE, PSNA College of Engineering & Technology, Dindigul- 624005, Tamil Nadu, India.
  • K. Manivannan School of Computer Science and Engineering, Jain (Deemed to be) University, Bengaluru, Karnataka, India




Abnormality detection, Artificial Neural Networks, CNN, Deep learning, integrated structure, segmentation


Deep learning-based anomaly detection in images has recently gained popularity as an investigative field with many global submissions. To simplify complex data analysis, researchers in the deep learning subfield of machine learning employ Artificial Neural Networks (ANNs) with many hidden layers. Finding data occurrences that significantly differ from generalizable to most data sets is the primary goal of anomaly detection. Many medical imaging applications use convolutional neural networks (CNNs) to examine anomalies automatically. While CNN structures are reliable feature extractors, they encounter challenges when simultaneously classifying and segmenting spots that need removal from scans. We suggest a separate and integration system to solve these issues, separated into two distinct departments: classification and segmentation. Initially, many network architectures
are taught independently for each abnormality, and these networks’ main components are combined. A shared
component of the branched structure functions for all abnormalities. The final structure has two branches: one
has distinct sub-networks, each intended to classify a particular abnormality, and the other for segmenting various abnormalities. Deep CNNs training directly on high-resolution images necessitate input layer image compression, which results in the loss of information necessary for detecting medical abnormalities. A guided GradCAM (GCAM) tuned patch neural network is applied to full-size images for anomaly localization. Therefore, the suggested approach merges the pre-trained deep CNNs with class activation mappings and area suggestion systems to construct abnormality sensors and then fine-tunes the CNNs on picture patches, focusing on medical abnormalities instead of training on whole images. A mammogram data set was used to test the deep patch classifier, which had a 99% overall classification accuracy. A Brain tumor image data set was used to test the integrated
detector’s ability to detect abnormalities, and it did so with an average precision of 0.99.