Breast cancer is the most frequent cause of cancer induced death among women in the
world. Diagnosis of this cancer can be done through radiological, surgical, and pathological …

Due to the complexity of medical images, traditional medical image classification methods
have been unable to meet actual application needs. In recent years, the rapid development …

To diagnose breast cancer, the number of mitotic cells present in histology sections is an
important indicator for examining and grading biopsy specimen. This study aims at …

Screening of microscopic slides is a manual process which involves its subjectivity. A semi-
automated computer based system can contribute to the detection of screening error by the …

A heuristic-based, multineural network (MNN) image analysis as a solution to the
problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in …

Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate
mainly due to the increased rate of smoking. This work aims at developing a sputum …

Imprint cytology (IC) refers to one of the most reliable, rapid and affordable techniques for
breast malignancy screening; where shape variation of H&E stained nucleus is examined by …

Molar pregnancy (also known as hydatidiform mole, hydatid mole, gestational trophoblastic
disease) represents forms of abnormal conception caused by defective fertilisation resulting …

Deep learning has the following problems in medical image classification application: first, it
is impossible to construct a deep learning model hierarchy for medical image properties; …

Contrast enhancement is one of the important steps in image processing. Enhancement
process has a vital role in medical image processing. Histogram Equalization (HE) plays the …