Accurate and rapid identification of dipteran vectors is integral for entomological surveys and is a vital component of control programs for mosquito-borne diseases. Conventionally, morphological features are used for mosquito identification, which suffer from biological and geographical variations and lack of standardization. We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for protein profiling of mosquito species from North India with the aim of creating a MALDI-TOF MS database and evaluating it. Mosquito larvae were collected from different rural and urban areas and reared to adult stages. The adult mosquitoes of four medically important genera, Anopheles, Aedes, Culex and Armigerus, were morphologically identified to the species level and confirmed by ITS2-specific PCR sequencing. The cephalothoraces of the adult specimens were subjected to MALDI-TOF analysis and the signature peak spectra were selected for creation of database, which was then evaluated to identify 60 blinded mosquito specimens. Reproducible MALDI-TOF MS spectra spanning over 2–14 kDa m/z range were produced for nine mosquito species: Anopheles (An. stephensi, An. culicifacies and An. annularis); Aedes (Ae. aegypti and Ae. albopictus); Culex (Cx. quinquefasciatus, Cx. vishnui and Cx. tritaenorhynchus); and Armigerus (Ar. subalbatus). Genus- and species-specific peaks were identified to create the database and a score of > 1.8 was used to denote reliable identification. The average numbers of peaks obtained were 55–60 for Anopheles, 80–100 for Aedes, 30–60 for Culex and 45–50 peaks for Armigeres species. Of the 60 coded samples, 58 (96.67%) were correctly identified by MALDI-TOF MS with a score > 1.8, while there were two unreliable identifications (both Cx. quinquefasciatus with scores < 1.8). MALDI-TOF MS appears to be a pragmatic technique for accurate and rapid identification of mosquito species. The database needs to be expanded to include species from different geographical regions and also different life-cycle stages to fully harness the technique for entomological surveillance programs.

Background: Malaria is a life-threatening disease caused by Plasmodium species, which is endemic in tropical and subtropical regions worldwide. In clinical settings, experienced parasitologists perform microscopic examinations of thick/thin blood slides. This method is labour-intensive and is adversely affected by inter- and intra-observer variability among the microscopists. The present study aimed to develop a malaria screening algorithm using computer vision to identify and classify malaria parasite-infected red blood cells (RBC) from microscopic blood slide images. Methods: The proposed classification methodology first employs digital image processing techniques, the watershed transform, to preprocess the raw images, followed by connected component labelling to accurately segment and isolate individual RBCs from the background. To classify these segmented cells as either normal or infected, convolutional neural networks (CNNs) were utilized, leveraging their ability to automatically extract relevant features through deep, hidden layers, thus eliminating the need for manual feature engineering. Results: To compare and determine the most effective classification engine, the study developed and evaluated five distinct models: four well-established transfer learning architectures (VGG16, VGG19, DenseNet121, and InceptionV3), alongside a newly proposed custom CNN model. A total of 2422 segmented RBC images were used for the training, and 692 different images were used for testing, with the VGG model showing the best accuracy at 99.57%. The proposed CNN architecture also showed competitive results with 99.14% accuracy. Conclusions: Transfer learning models demonstrated remarkable accuracy for malaria parasite classification from blood smear slides, with VGG19 (99.57%) achieving the highest accuracy on diverged datasets for the test images. The analysis demonstrates the potential of this approach as a computational aid for future image-based malaria screening in conjunction with existing diagnostic tests.