Abstract
Reconstruction of medical images is imperative for the comprehension of
clinical anomalies. Various processes and techniques are employed to generate efficient
anatomical representations of the human body in medical imaging. This technique
provides physicians with a visual depiction of internal organs, aiding in the verification
of ongoing treatments, accurate diagnoses, and treatment planning. Medical imaging
encompasses diverse methods such as ultrasound, X-rays, MRI, and CT scans, with the
choice depending on the condition of the ailment, such as kidney stone diseases, breast
cancer, and brain tumors. However, the quality of medical images can becompromised
due to different sources of noise and blurriness. This chapter introduces an advanced
image processing methodology to detect diseases in medical images, particularly brain
tumors, kidney stones, and breast cancer using ultrasound and MRI images. The
proposed approach involves converting RGB medical images into grayscale, removing
labels, and adjusting image intensity to enhance the contrast of biomedical images.
Median filtering is applied to eliminate noise, and the Discrete Wavelet Transform
(DWT) is utilized for brain tumor detection. The filtered medical image output is
subjected to morphological and k-means clustering segmentation. To classify the
images into two categories benign and malignant, Convolutional Neural Network
(CNN) classifiers are employed. The final system analysis involves evaluating,
specificity, accuracy, and sensitivity through the preparation of a confusion matrix. The
classification system demonstrates an accuracy of approximately 95%. This presented
technique holds potential in supporting doctors with early detection for precise patient
treatment.